Environmental Influences on Spatial Memory and the Hippocampus in Food-Caching Chickadees

Vladimir V. Pravosudov
Department of Biology, University of Nevada, Reno, USA

Timothy C. Roth II
Department of Psychology, Franklin and Marshall College, USA

Lara D. LaDage
Department of Biology, Penn State, Altoona, USA

Cody A. Freas
Department of Biological Sciences, Macquarie University, Australia

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Abstract

Cognitive abilities have been widely considered as a buffer against environmental harshness and instability, with better cognitive abilities being especially crucial for fitness in harsh and unpredictable environments. Although the brain is considered to be highly plastic and responsive to changes in the environment, the extent of such environment-induced plasticity and the relative contributions of natural selection to the frequently large variation in cognitive abilities and brain morphology both within and between species remain poorly understood. Food-caching chickadees present a good model to tackle these questions because they: (a) occur over a large gradient of environmental harshness largely determined by winter climate severity, (b) depend on food caches to survive winter and their ability to retrieve food caches is, at least in part, reliant on hippocampus-dependent spatial memory, and (c) regularly experience a distinct seasonal cycle of food caching and cache retrieval. Here we review a body of work, both comparative and experimental, on two species of food-caching chickadees and discuss how these data relate to our understanding of how environment-induced plasticity and natural selection generate environment-related variation in spatial memory and the hippocampus, both across populations as well as across seasons within the same population. We argue that available evidence suggests a relatively limited role of environment-induced structural hippocampal plasticity underlying population variation. At the same time, evidence is consistent with the history of natural selection due to differences in winter climate severity and associated with heritable individual variation in spatial memory and the hippocampus. There appears to be no clear direct association between seasonal variation in hippocampus morphology and seasonal variation in demands of food caching. Finally, we suggest that experimental studies of hippocampal plasticity with captive birds should be viewed with some caution because captivity is associated with large reductions in many hippocampal traits, including volume and in some cases neurogenesis rates, but not neuron number. Comparative studies using captive birds, on the other hand, appear to provide more reliable results, as captivity does not appear to override population differences, especially in the number of hippocampal neurons.

Acknowledgements: Vladimir Pravosudov was supported by NSF awards IOS1351295 and IOS0918268 and Lara LaDage was supported by NSF award IOS0918268. We would like to thank Chris Sturdy and three anonymous reviewers for their constructive criticisms that significantly improved the ­manuscript.

Keywords: spatial memory; hippocampus; neurogenesis; neurons; plasticity; natural selection; food caching; environment; winter; ambient temperature; seasonality; chickadee

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A key evolutionary question for understanding how environmental heterogeneity is associated with cognitive abilities concerns the relative contribution of environment-induced effects (e.g., plasticity) and natural selection acting on heritable cognitive traits as a means of generating environment-related variation in cognition and neural traits (e.g., Pravosudov & Roth, 2013). At least in humans, there is sufficient evidence that both general cognition and specific cognitive traits are highly heritable and that individual variation in these traits is, at least in part, determined by genetics (e.g., Ando, Ono, & Wright, 2001; Haworth et al., 2010; McGee, 1979; Pedersen, Plomin, Nesselroade, & McClearn, 1992; Plomin, Pedersen, Lichtenstein, & McClearn, 1994; Plomin & Spinath, 2002). Assuming that heritability of cognitive traits is not a unique human phenomenon but is common in other animals, it should provide ample opportunities for natural selection to generate variation in cognitive traits given different selection pressures. Many species occur over a large range of environmental conditions and experience major seasonal changes in their environment. Both geographic and seasonal variation in environmental conditions are likely to impart different demands on cognitive abilities, which may be especially important for fitness in harsher environments (e.g., longer winter period, lower temperatures, more snow cover covering foraging substrates and more frequent snowfalls, etc.) with higher energetic demands (due to lower temperatures) and a shortage of naturally available food (e.g., Pravosudov & Clayton, 2002; Pravosudov & Roth, 2013). It is important to note that the range of seasonal variation is usually also associated with geographic variation with a larger range of seasonal variation in harsher environments (e.g., more northern environments are associated with stronger seasonal differences).

Food-caching chickadees present a good case to understand the relationship between the environment, cognition, and the brain because (a) they occur over a large gradient of environmental harshness with different demands on caching and cache retrieval, (b) caching and cache retrieval depend, at least in part, on hippocampus-dependent spatial memory, and (c) they exhibit highly seasonal food caching behavior.

Population Variation in Spatial Memory and Hippocampus Morphology Is Associated with Differences in Winter Climate Harshness

Food-caching chickadees occur over a large range of environmental conditions with some populations experiencing relatively milder winters and some others experiencing relatively harsher winters. Chickadees are non-migratory birds that spend the non-breeding season in social groups characterized by linear social dominance hierarchy (e.g., Ekman, 1989; Hogstad, 1989) and appear to rely on food caches to survive winters (e.g., Pravosudov & Smulders, 2010). Most food-caching chickadee species live in temperate climates where the highest rates of mortality likely occur during the winter, likely due to the inability to meet energetic requirements. During the winter naturally available food is both in short supply and unpredictable in availability. Thus, food caching has been widely hypothesized to have evolved to provide a more reliable food supply during that time (Krebs, Sherry, Healy, Perry, & Vaccarino, 1989; Pravosudov & Clayton, 2002; Pravosudov & Roth, 2013; Sherry, Vaccarino, Vuckenham, & Herz, 1989; Vander Wall, 1990). At the same time, the large variation in winter harshness associated with climate severity (colder temperatures, more snowfall, longer winter period) across species ranges might be expected to influence the reliance on food caches, depending on winter climate (Pravosudov & Clayton, 2002; Pravosudov & Roth, 2013). Longer winter periods means longer periods without abundant and predictable food supply associated with phenology of main natural food sources (e.g., invertebrates). Colder temperature is likely associated with higher food intake requirements, yet during the winter naturally available food is limited and unpredictable, and more snow (covering both ground and frequently tree branches) likely reduces access to already limited food. In food-caching birds, food caches appear to represent the main reliable food source during the winter, and harsher winter conditions can be expected to increase reliance on food caches for overwinter survival.

It is well established that spatial memory plays a role in successful cache retrieval and, potentially, even in generating the optimal density of caches during caching (e.g., Male & Smulders, 2007), so variation in winter climate harshness could be expected to produce differential demands on spatial memory ability (Pravosudov & Roth, 2013). Birds living in harsher winter environments should benefit from a superior spatial memory that allows them to be more successful in retrieving previously made caches compared to birds wintering in milder climates (Pravosudov & Clayton, 2002). As spatial memory is dependent, at least in part, on the hippocampus, differences in spatial memory among populations that are due to differential dependence on food caches for survival should also be associated with differences in the hippocampus (Pravosudov & Roth, 2013). Such expected differences in spatial memory and the hippocampus might come about via environment-induced plastic phenotypic responses associated with the differential use of memory (Clayton, 1996, 2001; Clayton & Krebs, 1994; Woollett & Maguire, 2011) and/or could be based on genetic differences produced by natural selection if differences in memory and hippocampus morphology are based on heritable mechanisms (Krebs et al., 1989; Pravosudov & Roth, 2013; Sherry et al., 1989). Before discussing the origin of potential population differences in spatial memory and the hippocampus, we shall first consider the data demonstrating such population differences.

Our studies focused on two species of food-caching chickadees—the black-capped chickadee (Poecile ­atricapillus) and the mountain chickadee (P. gambeli). Black-capped chickadees occur over a large range on the North American continent that spans large variation in winter conditions both longitudinally and latitudinally (Figure 1; Pravosudov & Clayton, 2002). Along the latitudinal gradient of winter climate harshness, the black-capped chickadee range expands from a milder climate in Kansas to a much harsher winter climate in Alaska, whereas along the longitudinal gradient, chickadees range from milder climate in Washington state to much harsher winter climate in Maine (Figure 1). The first study compared chickadees from the two most different populations (from most extremely different winter environments) from Alaska (Anchorage) and Colorado and reported that chickadees from Alaska (harsh winters) had a stronger propensity to cache food, significantly better spatial, but not nonspatial memory ability, larger relative and absolute hippocampus volume, and a significantly larger total number of hippocampal neurons (Pravosudov & Clayton, 2002). The follow-up studies (Roth, LaDage, & Pravosudov, 2011; Roth & Pravosudov, 2009) compared 10 populations of black-capped chickadees along the winter climate gradient, including the two populations previously compared in Pravosudov and Clayton (2002). These studies showed that independent of latitudinal differences in day length (shorter in northern populations), harsher winter climatic conditions were associated with larger hippocampus volume, higher total number and larger soma size of hippocampal neurons, larger total number of hippocampal glial cells, and higher neurogenesis rates (Figure 2; Chancellor, Roth, LaDage, & Pravosudov, 2011; Freas, Bingman, LaDage, & Pravosudov, 2013; Roth et al., 2011; Roth & Pravosudov, 2009).

Figure 2. Hippocampus volume (A, B, D), total number of hippocampal neurons (A, B, D), and adult hippocampal neurogenesis rates (C, D) in blackcapped chickadees sampled directly from the wild without experiencing any captive environment across latitudinal (A, C) and longitudinal (B) gradient of winter climate harshness and in captive chickadees hand-reared from 10 days of age and maintained in controlled laboratory conditions throughout their entire life (D). From Roth & Pravosudov (2009), Roth et al. (2011), and Roth et al. (2012).

Mountain chickadees experience different winter conditions on a much smaller spatial scale along an elevation gradient of winter climate severity in the mountains, with birds at higher elevations experiencing longer and colder winters (Freas, LaDage, Roth, & Pravosudov, 2012). Higher elevations are associated with significantly lower winter temperatures (likely requiring more food intake to meet higher energetic demands), longer winter period associated with limited natural (e.g., not cached) food supply (likely increasing reliance on food caches for overwinter survival), and significantly more snow cover (both on the ground and on trees) that likely limits access to some potential foraging substrates. Similarly to black-capped chickadees from different winter conditions, mountain chickadees from higher elevations in the Sierra Nevada had a stronger propensity to cache food, better spatial memory ability, larger hippocampus volume, higher total number and larger soma size of hippocampal neurons, and higher hippocampal neurogenesis rates (Figure 3; Freas et al., 2012; Freas, Bingman, et al., 2013; Freas, Roth, LaDage, & Pravosudov, 2013).

Figure 3. Hippocampus volume (A, D), total number of hippocampal neurons (B, E), adult hippocampal neurogenesis rates (C), and telencephalon (minus the hippocampus) volume (F) in mountain chickadees sampled at different elevations directly from the wild (without experiencing captive conditions; A, B, C) and in chickadees captured as juveniles and maintained in the same controlled laboratory conditions for several months (D, E, F — filled circles; open circles represent birds sampled directly from the wild for comparison). From Freas et al. (2012) and Freas, Bingman, et al. (2013).

Overall, these combined data on 10 populations of black-capped chickadees (with the data on two of these populations collected twice during different years) and on mountain chickadees from three different elevations are highly consistent in showing significant differences in food caching propensity, spatial memory, and hippocampus morphology related to winter climate. This pattern is, in turn, consistent with the hypothesis that population variation associated with differences in winter climate might be produced by natural selection acting on food caching–related spatial memory (Pravosudov & Roth, 2013).

Harsher environments are likely associated with increased reliance on food caches for overwinter survival and therefore should favor more intense food caching and better spatial memory ability needed to recover food caches. Differential winter mortality based on individual variation in food caching propensity, spatial memory, and hippocampus morphology supporting spatial memory might be expected to result in evolutionary changes in both memory and its neural mechanisms (Pravosudov & Roth, 2013). It is also possible that both memory and hippocampus morphology flexibly adjust to local conditions (e.g., Clayton & Krebs, 1994; Woollett & Maguire, 2011), and that climate-dependent population variation is a product of such environment-induced phenotypic plasticity.

Potential Causes of Climate-Related Variation in Spatial Memory and the Hippocampus

Understanding the causes of climate-dependent population variation in spatial memory and the hippocampus is important for our understanding of both the evolution of cognition and how animals might respond to changing environments and to changes in climate. Most data available so far point toward natural selection acting on heritable mechanisms underlying individual differences in spatial memory and the hippocampus as the main driver for the observed climate-related variation in food-caching chickadees in the following ways:

1. Population differences in both species have been detected in juvenile birds prior to experiencing their first winter conditions even though climatic conditions during late summer and early autumn do not appear to be energetically challenging, food is usually superabundant, and chickadees mostly cache, but do not retrieve their long-term food caches (e.g., Pravosudov, 1983).
2. In both species, laboratory conditions did not eliminate population differences in food caching rates, spatial memory performance, and some hippocampal properties (most notably the total number of neurons; Freas et al., 2012; Freas, Bingman, et al., 2013; Pravosudov & Clayton, 2002).
3. In black-capped chickadees, birds from the two extreme populations (Alaska and Kansas) were hand-reared from the nestling age when the eyes were still closed (10 days of age) and maintained in controlled laboratory conditions during their entire life. Yet hand-reared chickadees from Alaska showed higher food caching rates, displayed better spatial memory performance, were better at novel problem solving, and had significantly larger total number and soma size of hippocampal neurons, higher total number of glial cells, and higher hippocampal neurogenesis rates (Freas, Roth, et al., 2013; Roth, LaDage, Freas, & Pravosudov, 2012). At the same time, the total number of hippocampal neurons and hippocampal neurogenesis rates were statistically similar between wild-caught and “common garden” chickadees from their respective populations. Even though the reason remains unknown, stable number of total neurons and higher neurogenesis rates in Alaska chickadees suggest higher cell death compared to more southern birds.
4. Significant differences in hippocampal gene expression were detected between “common garden” black-capped chickadees hand-reared from the two extremely different environments in genes known to be involved in neurogenesis and other hippocampal processes even though these birds spent their entire life (from day 10 of age) in the same controlled laboratory conditions (Pravosudov et al., 2013).

All of these data suggest, albeit indirectly, that population differences are unlikely to be a direct plastic response to variation in environmental conditions associated with differential demands for food caches. It remains potentially possible, however, that population differences arise following some triggers during early life or during development. If so, it appears unlikely that the nature of the potential triggers concerns some differences in food caching–related experiences. It has been shown that memory-based caching experiences are critical for hippocampus development, yet it appears that just a few caching and cache-retrieval experiences are sufficient for full hippocampus development (Clayton, 1996, 2001; Clayton & Krebs, 1994). Considering that both black-capped and mountain chickadees cache thousands of food items starting in later summer (Brodin, 2005), it is clear that chickadees in all populations exceed the minimum threshold shown to be critical for hippocampus development (Pravosudov & Roth, 2013). Yet, even when food caching was severely limited in laboratory conditions both in chickadees collected as juveniles after having some food caching experiences and in chickadees hand-reared as nestlings prior to any caching experiences, significant differences in spatial memory performance and in most hippocampal properties remained (Freas et al., 2012; Freas, Roth, et al., 2013; Roth et al., 2012). Nevertheless, the possibility that climate-related differences in memory and the hippocampus are associated with epigenetic (e.g., developmental) or maternal (e.g., yolk hormones) effects remains viable and, as of yet, untested.

What Is Plastic in the Hippocampus: Experimental Studies

Although all studies so far have been unable to eliminate population differences in memory and the hippocampus by manipulating environmental conditions, these studies provided important information about the plasticity of the hippocampus and suggested that some hippocampal properties are very plastic (e.g., hippocampus volume, neuron soma size, total number of glial cells), but others are not (total number of hippocampal neurons).

Hippocampus Volume

Many studies testing the hypothesis that interspecific variation in hippocampus size represents adaptive specialization related to memory-dependent food caching behavior (e.g., Krebs et al., 1989; Sherry et al., 1989) used hippocampus volume as a dependent measure. Population comparisons of both black-capped and mountain chickadees also used hippocampus volume among many other hippocampal properties and reported significant climate-related differences (Freas et al., 2012; Freas, Roth, et al., 2013; Pravosudov & Clayton, 2002; Roth et al., 2011; Roth & Pravosudov, 2009). Yet, hippocampus volume is undoubtedly one of the most plastic of all hippocampal properties. Multiple studies documented that when chickadees and other passerine birds are brought into laboratory conditions, their hippocampus volume shrinks by about 30% (LaDage, Roth, Fox, & Pravosudov, 2009; Smulders, Shiflett, Sperling, & DeVoogd, 2000; Tarr, Rabinowitz, Imtiaz, & DeVoogd, 2009). Hippocampus volume in black-capped chickadees that have been hand-reared and maintained in controlled laboratory conditions was also significantly smaller than that in chickadees sampled directly from the wild and without any period of captivity (Roth et al., 2012).

The effect of memory-based experiences on the development of the hippocampus has been well documented for young, inexperienced-in-food-caching parids (Clayton, 1996, 2001; Clayton & Krebs, 1994). If inexperienced young birds are deprived of food caching and cache retrieval experiences, their hippocampus volume remains smaller than that of adults or young birds provided such experiences. Most important, only a few caching experiences are needed for the hippocampus to reach its full volume, and further experiences do not result in any additional increases in volume (Clayton, 2001; Clayton & Krebs, 1994). At the same time, restriction of memory-based experiences in “experienced” birds has been suggested to result in hippocampus volume reductions (Clayton & Krebs, 1994). This latter finding, however, was not supported by another study using wild-caught birds in a controlled laboratory environment, which showed no differences in hippocampus volume between experienced mountain chickadees deprived of food caching and cache retrieval experiences for several months and chickadees regularly engaged in these activities (LaDage et al., 2009).

It is unclear which specific mechanisms result in captivity-related changes in hippocampus volume. For birds caught as juveniles/adults and brought into captive laboratory conditions, captivity-related stress is a likely cause (Roth et al., 2012). At the same time, experimental manipulations of memory use and food caching and retrieval in captive conditions failed to produce significant differences in hippocampus volume (LaDage et al., 2009), which suggests that memory use alone might not have a strong effect on hippocampus volume in experienced birds.

It is also possible that memory use does not show any effects on hippocampus volume specifically in captive birds, which already have a much reduced hippocampus volume due to captive environment. Yet manipulations of memory use in captivity do have an effect on other hippocampal processes such as adult neurogenesis rates (LaDage, Roth, Fox, & Pravosudov, 2010). In contrast to avian studies, human learning experiences are correlated with posterior hippocampus volume (Woollett & Maguire, 2011), but there were no structural changes in individuals who trained, but failed to learn spatial information. It remains unclear, however, what exactly did change in the human hippocampus that resulted in an increased volume.

Seasonal changes in food caching are associated with changes in day length, yet photoperiod manipulations in captive chickadees aimed to simulate seasonal changes in day length also failed to generate significant differences in hippocampus volume, even though such manipulations affected food caching rates (Hoshooley, Phillmore, & MacDougall-Shackleton, 2005; Krebs, Clayton, Hampton, & Shettleworth, 1995; MacDougall-Shackleton, Sherry, Clark, Pinkus, & Hernandez, 2003).

All in all, hippocampus volume exhibits a large degree of plasticity, but it remains unclear whether such plasticity is memory dependent in fully developed, experienced food-caching chickadees.

Hippocampal Neuron Soma Size

In both black-capped and mountain chickadees, hippocampal neuron soma size was significantly associated with winter climate severity, with birds in harsher environments having larger hippocampal neuron soma (Figure 4; Freas, Bingman, et al., 2013). Similar to the hippocampus volume, hippocampal neuron soma size appears highly plastic, and captivity resulted in significant soma size reduction in both black-capped and mountain chickadees (Figure 4; Freas, Bingman, et al., 2013; Freas, Roth, et al., 2013). Furthermore, it appears that captivity specifically affected neuron soma size in the hippocampus but not in the areas adjacent to the hippocampus (Freas, Bingman, et al., 2013). Despite significant reduction in hippocampal neuron soma size due to captive conditions, population differences remained significant in the hand-reared black-capped chickadees from the two extremely different environments (Freas, Bingman, et al., 2013). The fact that chickadees from the harsher environment still had significantly larger hippocampal neuron soma even though they spent their entire life (from day 10 of age) in the same controlled laboratory environment as chickadees from the milder environment suggests that these differences are regulated, at least in part, by some heritable mechanisms.

Figure 4. Mean hippocampal neuron soma size in wild black-capped chickadees (A) along environmental gradients and in wild-caught mountain chickadees (B) from different elevations. Mean hippocampal neuron soma size (C) as well as neuron soma size in brain area HA (G) and M — mesopallium (D) in mountain chickadees from a single elevation (mid) sampled directly from the wild and captured as juveniles. These birds were maintained in laboratory conditions under two treatments: deprived (no food caching and cache retrieval experiences) and experienced (regular food caching and cache retrieval experiences). Mean hippocampal neuron soma size in black-capped chickadees (E) from two environments at the extremes of the winter harshness range sampled directly from the wild (filled circles) and hand-reared and maintained in controlled laboratory environment (open circles). Mean hippocampal neuron soma size in mountain chickadees (F) from two elevations, both sampled directly in the wild (open circles) and captured as juveniles, but maintained in a controlled laboratory environment (filled circles). From Freas, Bingman, et al. (2013).

Similar to hippocampus volume, it remains unclear what exactly causes the reduction in hippocampal neuron soma size associated with a captive environment. Experimental manipulation of memory-based food caching and cache recovery did not produce any detectable effects on hippocampal neuron soma size, yet this manipulation did have a significant effect on hippocampal neurogenesis rates (Freas, Bingman, et al., 2013). So it is possible that neuron soma size reduction might be due to stress associated with captivity in birds captured as juveniles or adults (as in LaDage et al., 2009; LaDage et al., 2010). On the other hand, neuron soma were also significantly smaller in the “common garden” black-capped chickadees, which spent their entire life in controlled laboratory conditions and it is unlikely that these birds experienced captivity-associated stress similar to wild-caught birds (Freas, Bingman, et al., 2013). For example, hippocampal neurogenesis rates in these “common garden” birds were statistically indistinguishable from those in wild-caught birds that experienced natural, and unquestionably much richer, environments (Roth et al., 2012).

Overall, experimental results suggest that environment-related changes in hippocampus volume could be at least partially due to changes in hippocampal neuron soma size. Interestingly, captivity had no effect on telencephalon volume in chickadees (Freas, Roth, et al., 2013; LaDage et al., 2009) and also no effect on neuron soma size in telencephalic areas adjacent to the hippocampus (Freas, Bingman, et al., 2013). While it is extremely likely that captivity-associated stress is one of the drivers for such changes, it remains unclear how memory-related experiences might affect hippocampal neuron soma size. At least in captive birds collected as juveniles from the wild, manipulating the number of memory experiences failed to produce a detectable effect on hippocampal neuron soma size (Freas, Bingman, et al., 2013).

Hippocampal Glia Numbers

The total number of hippocampal glial cells was significantly different between the two populations of black-capped chickadees from extremely different environments, with birds from harsher environment having more glia (Figure 5; Roth, LaDage, Chavalier, & Pravosudov, 2013). At the same time, the number of glia also showed environment-induced plasticity as chickadees that were hand-reared and maintained in the same controlled laboratory environment had significantly fewer hippocampal glia cells compared to juvenile wild-caught birds (Roth et al., 2013). Both population- and captivity-related differences in the number of hippocampal glia closely followed differences in hippocampus volume and in hippocampal neuron soma size, which suggest that plasticity in the hippocampus volume is likely due, at least in part, to changes in the number of glia. At the same time, population differences in glia still remained significant even in birds that were hand-reared and maintained in the same controlled laboratory environment—a result that suggests involvement of some heritable mechanisms underlying population differences (Roth et al., 2013). Overall, it appears that the number of hippocampal glia cells is both plastic and, to a degree, controlled by some heritable mechanisms, which might respond to selection pressure associated with environmental differences.

Hippocampal Neuron Numbers

In both black-capped and mountain chickadees, significant population differences in the total number of hippocampal neurons was associated with winter climate harshness (Freas et al., 2012; Pravosudov & Clayton, 2002; Roth et al., 2011; Roth & Pravosudov, 2009). Chickadees from harsher environments had significantly more hippocampal neurons. In contrast to all other, previously discussed hippocampal properties, the total number of neurons does not appear plastic. A captive environment resulted in significant reductions in hippocampus volume, neuron soma size, and glial numbers, but not in the total number of neurons (Freas, Bingman, et al., 2013; Freas, Roth, et al., 2013; LaDage et al., 2009). In mountain chickadees, two independent studies confirmed that a period of several months in captivity produced no significant effects on the total number of hippocampal neurons in birds collected as experienced juveniles (Figures 4, 6; Freas, Roth, et al., 2013; LaDage et al., 2009). In black-capped chickadees, birds that were hand-reared and maintained in the same controlled laboratory environment had a statistically similar total number of hippocampal neurons to chickadees sampled as experienced juveniles in their natural environment (Figure 2; Roth et al., 2012). Furthermore, in both species, there were significant differences related to variation in winter climate in the number of hippocampal neurons both in wild-caught and captivity-maintained individuals (Freas, Roth, et al., 2013; Roth et al., 2012). Therefore, whereas population differences in hippocampus volume were associated with differences in the total number of hippocampal neurons, within-population changes in hippocampus volume were independent of the total number of neurons. These results suggest that the total number of hippocampal neurons is most likely controlled by some heritable mechanisms, which could be acted upon by natural selection. While at least some population variation in hippocampus volume might be due to potential differences in experiences, population variation in the total number of hippocampal neurons does not appear to be influenced directly by the environment. Even when hippocampus volume was reduced by as much as 30% in captivity, the number of neurons appeared to remain unchanged. So, the number of neurons might serve as a more rigid hippocampus structure, while the neuron soma size (and likely associated arborization/connectivity) and the number of glial cells are prone to changes due to immediate environmental conditions, which could produce changes in hippocampus volume independent of the number of neurons.

Hippocampal Neurogenesis

Adult hippocampal neurogenesis, a process of production, survival, and recruitment of new neurons in the hippocampus, has been generally linked to spatial learning (e.g., Barnea & Pravosudov, 2011). As food-caching birds appear to rely on spatial memory to recover their food caches, hippocampal neurogenesis is likely an important process that might potentially be under selection. A two-species comparison indeed showed that a food-caching species had significantly higher hippocampus neurogenesis rates (Hoshooley & Sherry, 2007). In both black-capped and mountain chickadees, adult hippocampus neurogenesis rates (estimated as the number of new immature neurons) were significantly associated with winter climate harshness, with birds from harsher climates having higher neurogenesis rates (Figures 2, 3; Chancellor et al., 2011; Freas et al., 2012). These population differences were in general agreement with the data on all other hippocampal properties: harsh winter climate was associated with larger hippocampus volume, larger total number and soma size of hippocampal neurons, larger total number of hippocampal glia cells and higher adult hippocampal neurogenesis rates. The question is whether these climate-related population differences in neurogenesis rates reflect plastic adjustments to local conditions and experiences or whether these differences might be, at least in part, controlled by some heritable mechanisms.

Results of experimental studies in food-caching birds suggest that adult hippocampal neurogenesis is significantly reduced in captive chickadees captured as experienced juveniles or adults (Figure 6; Barnea & Nottebohm, 1994; LaDage et al., 2010), and that spatial memory experiences additionally affect hippocampal neurogenesis rates in wild-caught captive chickadees (LaDage et al., 2010). Mountain chickadees maintained in captive laboratory conditions, but allowed to engage in memory-based food caching and cache retrieval, had significantly higher neurogenesis rates compared to captive chickadees denied such experiences. At the same time, even experienced chickadees had significantly, and much lower, hippocampal neurogenesis rates than birds sampled directly from the wild (i.e., trapped and sacrificed without experiencing captivity; LaDage et al., 2010).

Figure 6. Effect of captivity and food caching related memory use on telencephalon (minus the hippocampus) volume (A), hippocampus volume (B), total number of hippocampal neurons (C) and adult hippocampal neurogenesis rates (D, E) in mountain chickadees. From LaDage et al. (2009, 2010).

Tarr et al. (2009) was so far the only study that reported no significant effect of captivity on new hippocampal neuron survival in black-capped chickadees—a result that is strikingly different from those reported in at least two other studies (Barnea & Nottebohm, 1994; LaDage et al., 2010). It is unclear why there was such discrepancy among the studies; in addition, Tarr et al. (2009) used methods that differ from those in all other studies. For example, Tarr et al. (2009) used multiple covariates, such as body mass, brain mass, and telencephalon volume, including the hippocampus in their analyses of the effect of captivity on the number of new cells. Use of these continuous variables as covariates can significantly affect the results concerning the effect of captivity on neuron survival rate, yet the effect of captivity on these variables has not been reported. Using the hippocampus volume as part of the overall telencephalon volume might confound the results, as the hippocampus volume is known to be affected by captivity. The question is whether new neuron survival is affected independently of any changes in the hippocampus volume. Unfortunately, Tarr et al. (2009) did not report analyses based on raw numbers of new surviving neurons, so it remains unclear whether there was an effect of captivity on the total number of new neurons. Barnea and Nottebohm (1994) reported significant reduction in new neuron survival in captive black-capped chickadees. In mountain chickadees, captivity resulted in a more than 30% reduction in the number of new immature neurons, although because of the methods used to label new neurons, this number represents a combination of new immature neurons of different age and therefore combines new neuron production and neuron survival (LaDage et al., 2010).

To add more confusion, there were no significant differences in adult neurogenesis rates (combined new neuron production and survival) between black-capped chickadees sampled directly from the wild and birds hand-reared and maintained in controlled laboratory conditions for many months (Figure 2; Roth et al., 2012). The only difference between the “common garden” study and all other chickadees studies mentioned above was that captive birds in the “common garden” study have never experienced “the wild,” while in the other studies wild-caught experienced birds were brought into the lab. Such results suggest that captivity-related differences in neurogenesis rates might be directly affected by stress of captivity in wild-caught birds, whereas hand-reared birds might not be affected by such stress (Roth et al., 2012). It is also likely that most laboratory rodent and avian studies showing environmental effects on hippocampal neurogenesis (e.g., review in Barnea & Pravosudov, 2011) also detect neurogenesis rates much below the normal “base” levels, which could indeed be improved by even slight environmental changes in extremely impoverished lab conditions. For example, Hall et al. (2014) reported significant effects of flight exercise on adult neurogenesis using doublecortin staining to quantify neurogenesis in adult starlings (Sturnus vulgaris) captured and maintained in a laboratory. The number of new neurons reported in Hall et al. (2014) is much smaller than that reported for wild chickadees using the same method (LaDage et al., 2010; Roth et al., 2012). Even though starlings are not a food-caching species and so likely have lower levels of hippocampal neurogenesis (Hoshooley & Sherry, 2007), it is also very likely that these numbers are much reduced due to captivity and so additional exercise might simply reduce captivity-related stress’s effect on neurogenesis, rather than have an additive effect on the naturally present baseline. Interestingly, photoperiod manipulations designed to imitate seasonal day length changes associated with seasonal variation in food caching activity also failed to produce any significant differences in hippocampal neurogenesis rates in captive birds (Hoshooley et al., 2005), even though such manipulations are known to affect food caching rates (MacDougall-Shackleton et al., 2003).

The major question is whether there is a threshold after which additional enhancements do not have any effects on neurogenesis. Our “common garden” experiment results certainly point in that direction as unstressed, hand-reared birds maintained in a relatively enriched captive environment (large cages, unrestricted food caching experiences) have similar hippocampal neurogenesis rates to the wild birds that experience an immensely richer natural environment. At the same time, memory experiences in likely stressed captive birds captured as juveniles or adults appear to ameliorate the negative effect of stress on neurogenesis (LaDage et al., 2010). Interestingly, food caching–related learning experiences have also been reported to increase hippocampal neurogenesis rates in juvenile “experience-naïve” hand-reared marsh tits (P. palustris; Patel, Clayton, & Krebs, 1997), but such an increase appears to be related to the initial memory experiences responsible for hippocampus growth and development rather than to experience-based adult neurogenesis in experienced birds.

The question remains, however, whether any additional experiences would also lead to increased neurogenesis rates given a hypothetical threshold. Results with “common garden” chickadees are certainly consistent with the ­threshold hypothesis as it would be difficult to explain otherwise why birds that spent their entire life in laboratory conditions had statistically indistinguishable neurogenesis rates from their conspecifics in natural conditions in the wild. These results also suggest that mechanisms regulating adult hippocampal neurogenesis rates might be heritable and therefore a potential target for natural selection acting on spatial memory.

It is also possible that food-caching species might be different from other non-caching species in maintaining hippocampal neurogenesis at high levels at all times. For example, hippocampal neurogenesis rates were almost three times as high in food-caching black-capped chickadees as in non-caching house sparrows (Passer domesticus) even after spending six weeks in captivity (Hoshooley & Sherry, 2007).

Conclusions of Experimental Studies

Experimental studies manipulating environment/experiences in food-caching chickadees suggest that most hippocampal properties, with the exception of neuron number, are likely both plastic and at the same time controlled by some heritable mechanisms. Environment-induced plasticity in hippocampus volume appears to be related to plasticity in hippocampal neuron soma size and the number of glial cells, but not in the total number of neurons. The total number of hippocampal neurons, on the other hand, appears to be fairly constant regardless of environmental manipulations, suggesting that it is regulated by some heritable mechanism(s).

Plastic changes due to experimental manipulations in hippocampus volume, neuron soma size, and the number of glia cells also do not override population differences associated with winter climate harshness, which further suggests that such differences are likely due, at least in part, to natural selection acting on food caching–related spatial memory. It appears that the main differences among populations are based on the differences in the total number of hippocampal neurons while neuron morphology (soma) and the number of glia cells exhibit additional experience-based variation. It remains unclear, however, how much of such variation is due to differences in memory-based experiences versus stress and whether any “positive” effects in laboratory studies are still well below the baseline natural levels.

Correlational Studies: Seasonal Variation

Food-caching birds present a good case to better understand plasticity of the brain because of the highly distinct seasonality in food caching behavior (Pravosudov, 2006). Food-caching parids such as chickadees cache tens of thousands of food items during late summer–early fall (e.g., long-term caching; Brodin, 2005) and might also cache again in spring (Pravosudov, 2006), while caching much less (e.g., short-term caching) during the winter and potentially not caching at all during summer.

The three studies that brought a large amount of interest to brain plasticity associated with food caching seasonality in black-capped chickadees showed that hippocampal neuron incorporation rates were higher during late autumn (Barnea & Nottebohm, 1994) and hippocampus volume and the total number of neurons were also highest during autumn (Smulders, Sasson, & DeVoogd, 1995; Smulders et al., 2000). Smulders et al. (2000) used birds from the Smulders et al. (1995) study and estimated the total number of hippocampal neurons based on the hippocampus volume. These latter two studies received especially visible attention from public media, which frequently stated that food-­caching chickadees can enlarge their hippocampi by 30% every year. Unfortunately, all available evidence combined (see below) does not support these initial claims.

First, even the initial studies provided conflicting information about seasonal changes in the number of neurons. Smulders et al. (2000) reported significant seasonal variation in the total number of hippocampal neurons, but Barnea and Nottebohm (1994) failed to detect such seasonal variation in the same species while reporting variation in hippocampal neuron incorporation rates only. At least two additional studies also failed to replicate results reported in Smulders et al. (1995) and Smulders et al. (2000) by showing no significant seasonal variation in both hippocampus volume and the total number of hippocampal neurons in black-capped chickadees (Hoshooley & Sherry, 2004; Hoshooley, Phillmore, Sherry, & MacDougall-Shackleton, 2007). These two latter studies also reported somewhat conflicting results on seasonal variation in adult hippocampal neurogenesis; Hoshooley and Sherry (2004) failed to detect significant seasonal variation in new neuron survival over 1–2 weeks, but Hoshooley et al. (2007) reported significantly higher new neuron survival rates over a 1-week period in January. Finally, Hoshooley and Sherry (2007) reported that chickadees sampled in autumn (October–November) had significantly smaller hippocampus volume and smaller number of hippocampal neurons compared to chickadees sampled in spring (March–April), a result that goes directly against the initial reports of a larger hippocampus in autumn (Smulders et al., 1995). At the same time, Hoshooley and Sherry (2007) detected no significant differences in hippocampal neurogenesis rates (new neuron survival over 6 weeks) between chickadees sampled in autumn and in spring. Finally, experimental manipulations of photoperiod in laboratory-maintained chickadees failed to produce any significant differences in hippocampus volume or hippocampal neurogenesis rates despite significantly affecting food caching rates (Hoshooley et al., 2005; Krebs at al., 1995; MacDougall-Shackleton et al., 2003). Overall, these results do not seem to provide convincing support that any of the hippocampal properties vary consistently and specifically in relation to seasonal cycle of memory-based food caching and cache retrieval. So why are there such discrepancies among the studies?

Hippocampus Volume

Using the same species in generally similar environmental conditions (Ithaca, New York and London, Ontario), one study reported significant seasonal variation in hippocampus volume (Smulders et al., 1995), the other two detected no seasonal variation (Hoshooley et al., 2007; Hoshooley & Sherry, 2004), and the fourth actually reported that chickadees sampled in autumn had significantly smaller hippocampus volume compared to chickadees sampled in spring (Hoshooley & Sherry, 2007). There are a couple of potential explanations for these differences.

1. Birds have been sampled in different years and in different locations, so it is possible that seasonal variation was present only in some years or only at a particular location. If that were the case, it would suggest that seasonal variation in hippocampus volume is likely not a regular phenomenon, but it might sometimes occur. Considering that winter climate conditions might be expected to be somewhat similar at both locations, this explanation does not seem likely.
2. The two labs used different methods to generate hippocampus volume estimates. Smulders et al. (1995) adjusted hippocampus volumes for the overall brain shrinkage (measured as brain mass change after post perfusion fixation process), which showed significant seasonal variation. Hoshooley and Sherry (2004, 2007) and Hoshooley et al. (2007) did not use such an adjustment. It is unfortunate that Smulders et al. (1995) did not report their data without adjusting for potential brain shrinkage so that it would be possible to evaluate whether these differences between the studies might be due to such an adjustment. At the same time, the purpose of such an adjustment is not entirely clear since hippocampus volume is measured relative to the rest of the telencephalon. In other words, even if the entire brain shrinks more, the ratio of hippocampus to telencephalon should remain the same, assuming that shrinkage is not influenced by region. Adjusting for shrinkage, on the other hand, might potentially generate spurious results specifically in regard to the relative hippocampus volume.

Seasonal Variation in the Total Number of Hippocampal Neurons

Again, seasonal variation in the total number of hippocampal neurons was reported in a single study (Smulders et al., 2000), while two other studies reported no significant seasonal variation (Barnea & Nottebohm, 1994; Hoshooley & Sherry, 2004) and one study actually reported the opposite pattern by showing that chickadees sampled in autumn had a significantly smaller number of hippocampal neurons than chickadees sampled in spring (Hoshooley & Sherry, 2007). These studies did not use unbiased stereological methods (e.g., optical fractionator, West, Slomianka, & Gunderson, 1991) to estimate the total number of neurons, but instead either counted cells only in some nonrandomly chosen areas (e.g., Smulders et al., 2000) and/or seemed to use neuron densities (number of cells divided by volume). Cell density is directly dependent on hippocampus volume and any shrinkage/variation in volume due to tissue processing could potentially produce biased results when the hippocampus volume, but not the number of neurons (or vice versa), shows significant variation. The optical fractionator method provides an estimate that is independent of tissue shrinkage or other variation in volume that is not associated with changes in neuron numbers (e.g., West et al., 1991). The optical fractionator method does depend on the volume, as a larger volume would result in more counting frames, which are used to estimate the total number of neurons. However, unlike direct density estimates (e.g., number of cells divided by volume), the optical fractionator would produce the same estimate for the number of cells if different volumes were associated with the same number of neurons. Considering that at least two studies showed no significant differences in the total number of hippocampal neurons between wild and captive birds using stereological methods when the hippocampus volume differed by almost 30% (Freas, Roth, et al., 2013; LaDage et al., 2009), it does not seem likely that chickadees would exhibit regular significant seasonal variation in the total number of hippocampal neurons. In fact, black-capped chickadees sampled at almost the same time when Smulders et al. (2000) reported a significant peak in the number of neurons (October) had a statistically indistinguishable number of hippocampal neurons from those in chickadees that were hand-reared and maintained in controlled laboratory conditions and were sampled in spring (Roth et al., 2012). If the number of neurons reflected differences in memory-based food caching, it should be expected that wild chickadees at the peak of food caching should experience much higher memory demands than hand-reared birds living in relatively small cages, yet these two groups did not differ significantly in the total number of neurons (Roth et al., 2012). Finally, Hoshooley and Sherry (2007) also reported a higher number of hippocampal neurons in spring compared to autumn—a pattern opposite to the one suggested by Smulders et al. (2000).

While it is impossible to say why only one of the four studies was able to report seasonal differences in the number of hippocampal neurons, considering all correlational and experimental evidence, it does not appear likely that the number of hippocampal neurons regularly exhibits food caching–related seasonal variation.

Hippocampal Neurogenesis

Data on seasonal variation in hippocampal neurogenesis rates in food-caching chickadees is also quite inconsistent. First, Barnea and Nottebohm (1994) reported that hippocampal new neuron incorporation rates were highest in black-capped chickadees injected with new neuron marker in October and attributed these high rates to the peak of autumn food caching. Hoshooley and Sherry (2004, 2007) reported no significant seasonal variation in hippocampal neurogenesis rates in the same species, and Hoshooley et al. (2007) reported a peak in new hippocampal neuron survival rates in January (and potentially in April when neurogenesis rates were not statistically different from those sampled in January), much later than reported by Barnea and Nottebohm (1994).

Hippocampal neurogenesis is the only hippocampal attribute (among the ones considered here) that has indeed been experimentally linked to spatial memory use (LaDage et al., 2010). Based on such experimental evidence it might be plausible to expect that seasonal changes in memory use associated with food caching might indeed produce seasonal changes in hippocampal neurogenesis rates. Yet available evidence does not seem to provide unequivocal support for the idea that changes in hippocampal neurogenesis rates track seasonal changes in memory use associated with food caching.

It is likely that chickadees use spatial memory both when they make tens of thousands of food caches during later summer–early fall (e.g., Male & Smulders, 2007) as well as all throughout the winter when they recover these caches (see references in Pravosudov & Smulders, 2010). So it is not clear whether memory use (all aspects, including memory acquisition during caching, memory formation, and memory recall used either during cache retrieval or when making other caches relative to locations of previously made caches) should be higher during the peak of caching or the entire winter. See Barnea and Pravosudov (2011) for more discussion about neurogenesis.

If memory use is heaviest during the peak of caching, it might be expected that the highest neurogenesis rates should be in late August–September and early October at the latest (Pravosudov, 2006). If new neurons are needed for new memories, new neurons should be incorporated into the existing hippocampal circuits during that time and new neuron production could be triggered at the beginning of intense food caching in late August. Yet, Barnea and Nottebohm (1994) detected highest new neuron incorporation rates 6 weeks after injecting birds with a new neuron marker in October. So these new neurons were likely functional only in mid to late November, much later and after the peak of food caching and therefore unlikely related to memory needs associated with food caching (e.g., Barnea & Pravosudov, 2011). Results of Hoshooley and Sherry (2007) showed an even later peak in new neuron survival (January), which is not likely related to the food caching process.

If memory use is the highest during cache retrieval, it might be expected that food-caching chickadees use memory intensely during the entire winter, or at least during a few winter months, likely from November to February. The data from both Barnea and Nottebohm (1994) and Hoshooley et al. (2007) still do not fit such a pattern. Barnea and Nottebohm (1994) reported the highest neuron incorporation rates only in birds injected with new neuron marker in October (measured 6 weeks later—likely in late November), but not in birds injected in December even though cache retrieval memory use should be as high in January as in November. Hoshooley et al. (2007), on the other hand, reported the highest hippocampal neuron 1-week survival rates in birds sampled in January–February, yet new neuron survival rates were almost as high (and statistically indistinguishable from) new neuron survival rates in birds sampled in April–May, when cache retrieval should not be critical. At the same time, Hoshooley and Sherry (2004, 2007) did not detect any significant seasonal variation in hippocampal neurogenesis rates.

There are important differences between the Barnea and Nottebohm (1994), the Hoshooley and Sherry (2004), and the Hoshooley et al. (2007) studies concerning the measured period of new neuron survival (Barnea & Pravosudov, 2011). While Barnea and Nottebohm (1994) and Hoshooley and Sherry (2007) estimated 6-week survival, Hoshooley and Sherry (2004) and Hoshooley et al. (2007) measured 1–2 week survival. In the latter two studies and in Hoshooley and Sherry (2007), neuron survival was measured in captive birds, while Barnea and Nottebohm (1994) measured neuronal incorporation rates in free-ranging birds. Despite these differences, the observed patterns do not seem to fit any of the patterns predicted using seasonality of food caching and cache retrieval. One-to-two week survival might be potentially insufficient to detect important differences in neuron survival, as it may take more than 6 weeks for the new neurons to express adult phenotype (Hoshooley & Sherry, 2007), so the data presented in Hoshooley and Sherry (2004) and Hoshooley et al. (2007) might be more indicative of new neuron production rates. Yet, seasonal variation in 6-week survival rates reported in Barnea and Nottebohm (1994) still does not follow a pattern expected from seasonal variation in food caching and cache retrieval.

Finally, there are methodological differences concerning using tritiated thymidine (Barnea & Nottebohm, 1994) and BrdU (Hoshooley & Sherry’s studies) that might also produce potential differences in estimation of neurogenesis rates (Leuner, Glasper, & Gould, 2009).

Overall, the available data do not seem to provide clear evidence for robust food caching–related seasonal variation in adult hippocampal neurogenesis rates. While it is possible that there are some seasonal changes, they might be unrelated to food caching and associated with some other factors such as winter temperature or activity patterns. While chickadees captured as juveniles and maintained in captive conditions did show memory use–based increases in hippocampal neurogenesis, these increases did not compensate for the large captivity-related reduction in neurogenesis rates (LaDage et al., 2010). At the same time, black-capped chickadees hand-reared and maintained in laboratory conditions had statistically similar hippocampal neurogenesis rates (joint estimate of new neuron production and survival) to those in chickadees sampled directly from the wild during the peak of food caching (Roth et al., 2012). There is little doubt that birds in the wild must have more memory-based experiences than birds that spent their entire life in a relatively confined captive environment, yet such differences were not reflected by hippocampal neurogenesis rates. Such data are suggestive of some rather small threshold beyond which more experiences are not likely to produce an additional increase in hippocampal neurogenesis. Such a suggestion, however, remains a speculation at this point, and more data are needed to understand the patterns of association between memory use and neurogenesis.

Overall, there appears to be no clear evidence that the hippocampus undergoes robust and predictable seasonal changes associated specifically with food caching and/or cache retrieval. In fact, many studies reported no significant seasonal variation in any of the traits—hippocampus volume, total neuron numbers, or adult neurogenesis rates.

Overall Conclusions

Population comparisons of two species of food-caching chickadees experiencing different winter climate conditions provided highly consistent evidence of environment-related, strong variation in spatial memory, hippocampus morphology including hippocampus volume, total number and soma size of hippocampal neurons, total number of hippocampal glia, and adult hippocampal neurogenesis rates.

Experimental data suggest that some, but not all, of these hippocampal properties might be directly affected by the environment; however, in all cases the largest effects were due to captive environment. Memory-based experiences were only shown to up-regulate hippocampal neurogenesis rates in captive birds with neurogenesis rates already significantly reduced in captive conditions. All other hippocampal properties discussed here were unaffected by manipulations of such experiences. In contrast, birds that were hand-reared from an early age and maintained in a fairly enriched laboratory environment (large cages, ability to cache food in multiple substrates) had adult hippocampal neurogenesis rates statistically indistinguishable from those measured in wild birds in their immensely richer natural environment, which points toward a relatively small threshold in experiences beyond which adult neurogenesis rates do not appear to be affected by additional enriching experiences.

The fact that hippocampus volume might be affected by the environment without significant changes in the total number of neurons suggests that using neuron densities for evaluating cognitive abilities is not only incorrect, but could be misleading. For example, captivity is associated with a significant reduction in hippocampus volume, but not in the number of neurons, which results in higher density of hippocampal neurons in captive birds.

Most evidence is consistent with the hypothesis that climate-related population variation in spatial memory and hippocampus morphology is produced by natural selection associated with individual heritable variation in spatial memory and its neural mechanisms. The fact that the total number of neurons does not change, even in extremely impoverished captive conditions, suggests the involvement of some heritable regulatory mechanisms. While the hippocampus volume, total number of glia, and neuron soma size can and do respond to direct environmental changes, these changes appear to be anchored around the total number of neurons, which seems quite stable. Although it remains untested whether individual variation in spatial memory and hippocampal morphology in birds is heritable and based on genetic variation, there is evidence from human research showing heritability of general cognitive ability, spatial ability, and hippocampus volume, as well as its genetic basis (e.g., Ando et al., 2001; Haworth et al., 2010; McGee, 1979; Pedersen et al., 1992; Plomin et al., 1994; Plomin & Spinath, 2002; Sullivan, Pfefferbaum, Swan, & Carmelli, 2001). Finally, there appears to be no unambiguous evidence showing consistent seasonal variation in hippocampus morphology directly related to the seasonal cycle of food caching and cache retrieval. In fact, experimental data on the number of neurons suggests that at least the number of neurons is not likely to vary seasonally.

Overall, it appears that environment-induced plasticity in hippocampus morphology related to hippocampus volume, total number and size of hippocampal neurons, glia cell numbers, and even hippocampal neurogenesis rates might be anchored around the total number of hippocampal neurons, which appears to be regulated by some heritable mechanisms responsive to natural selection on food caching–related spatial memory. More research on hippocampus plasticity needs to be done on wild birds as captive conditions generate strong negative effects and all experience-based experimental manipulations in captive birds, especially captured as juvenile or adults, cannot come close to the baseline levels present in wild birds. Such strong captivity effects suggest that any results of experimental studies investigating brain plasticity should be considered cautiously.

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Developmental Stress and Correlated Cognitive Traits in Songbirds

Tara Farrell
University of Western Ontario

Buddhamas Kriengwatana
Leiden University

Scott A. MacDougall-Shackleton
University of Western Ontario

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Abstract

Early-life environments have profound influence on shaping the adult phenotype. Specifically, stressful rearing environments can have long-term consequences on adult physiology, neural functioning, and cognitive ability. While there is extensive biomedical literature regarding developmental stress, recent research in songbirds highlights similar findings in domesticated and non-domesticated species, opening up the field to broader questions with an ecological and evolutionary focus. Here, we review the literature in songbirds that exemplifies how developmental stress can shape birdsong, a sexually selected cognitive trait, and other physiological and cognitive abilities. Furthermore, we review how various traits can be correlated in adulthood as a result of various systems developing in tandem under stressful conditions. In particular, birdsong may be indicative of other cognitive abilities, which we explore in depth with current research regarding spatial cognition. In addition, we discuss how various personality traits can also be influenced by the intensity and timing of developmental stress (prenatal versus postnatal). We conclude by highlighting important considerations for future research, such as how assessing cognitive abilities is often constrained by experimental focus and more weight should be given to outcomes of reproductive success and fitness.

Author Note: Tara Farrell and Scott A. MacDougall-Shackleton, Department of Psychology and Advanced Facility for Avian Research, University of Western Ontario, 1151 Richmond Street, London, Ontario, Canada N6A 5B8; Buddhamas Kriengwatana, Institute of Biology Leiden (IBL), Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands.
Correspondence concerning this article should be addressed to Tara M. Farrell at tfarrel2@uwo.ca.

Keywords: developmental stress; birdsong; cognition; correlated traits; hippocampus; corticosterone; behavioral syndromes

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Organisms exhibit integrated phenotypes, with various traits working in concert to produce a functioning whole. Traits typically co-vary among individuals, and much research has attempted to determine the underlying causes of correlations between different traits. One such cause is the influence of environmental factors on developmental trajectories, which can induce pleiotropic effects on the adult phenotype. That is, environmental conditions do not impact the development of a single trait in isolation, but will affect numerous traits simultaneously, thus potentially leading to developmentally correlated traits (Metcalfe & Monaghan, 2001; Spencer & MacDougall-Shackleton, 2011). In the case of cognition and behavior, any neural developmental processes that are sensitive to environmental factors at the same time points in development may become developmentally correlated, even if they are functionally unrelated (Figure 1).

There has been growing interest in how stressful environmental factors can influence development. In one view, such developmental stressors can disrupt development and result in impaired performance. In another view, developmental stressors may program development to produce an adult better suited to a stressful environment (phenotypic programming; see Monaghan, 2008). If stressors impair development, then stressed individuals should have impairments in multiple traits. If stressors induce phenotypic programming, then stressed individuals should have multiple programmed traits. Regardless of these competing views, developmental stressors provide a potent mechanism by which multiple neural and cognitive traits may become developmentally correlated.

The aim of this review is to provide an overview of the literature regarding the effects of developmental stress on various cognitive-behavioral traits in songbirds. Cognition, defined for this review, refers to the processes that underlie the acquisition, processing, and storage of information, which an animal uses to interact within its environment (Shettleworth, 2009, p. 720). We explore how the developmental stress hypothesis—originally formulated to explain how developmental experiences can affect birdsong in particular—can be extended as a framework to help researchers understand how cognitive and behavioral traits in general may become correlated through developmental stressors. Consequently, this review will synthesize findings in songbirds regarding the developmental stress hypothesis, the implications that this hypothesis has for females, the reliability of song as a proxy of other cognitive abilities, and the effects of developmental stress on spatial cognition and personalities/behavioral syndromes—topics addressed by recent reviews of the developmental stress hypothesis that warrant a more thorough discussion (Buchanan, Grindstaff, & Pravosudov, 2013; MacDougall-Shackleton & Spencer, 2012; Spencer & MacDougall-Shackleton, 2011).

There are two primary reasons for focusing this review on songbirds despite the large and growing biomedical literature on the effects of early stressors on neural and cognitive development in domesticated species (e.g., Andersen & Teicher, 2008; Heim & Nemeroff, 2001; Heim, Shugart, Craighead, & Nemeroff, 2010; Welberg & Seckl, 2008). First, it is important to understand the role that stress plays in cognitive development in non-domesticated species because these studies provide valuable insights into the effects of developmental stress on ecologically relevant behaviors and sexual selection (e.g., MacDougall-Shackleton & Spencer, 2012; Buchanan et al., 2013). Second, birdsong is one of the most extensively studied areas of animal behavior and cognition, and depends critically on developmental experience. In addition, songbirds have been studied with respect to hippocampus-dependent spatial cognition, behavioral innovation, and behavioral syndromes, providing a rich and diverse animal model from which we can understand how stress affects a variety of cognitive functions.

Birdsong and the Developmental Stress Hypothesis

There has been a long-standing interest in the mechanisms of birdsong learning and development. Because birdsong is a sexually selected trait, there has also been extensive research on the types of information transmitted by birdsong and how song can act as an indicator signal to accurately provide information to the receiver regarding qualities of the singer. The current prevailing hypothesis to explain how song can be an honest signal of male quality is the developmental stress hypothesis (Nowicki, Hasselquist, Bensch, & Peters, 2000; Nowicki, Peters, & Podos, 1998). Because song learning and development of the neural structures underlying song occur over a protracted period of early life, they may be sensitive to a variety of stressors. Thus, a bird that sings a song of high quality is a bird that suffered relatively little stress during early life or was able to cope with such stress. Consequently, song may also provide predictive information about other traits that are sensitive to developmental stress (Nowicki et al., 1998). We will first review what is known about the effects of developmental stress on male song development and then discuss the effects of developmental stress on song learning and preference in the underrepresented female.

The developmental stress hypothesis has received substantial empirical support, as developmental stressors ranging from dietary manipulations, glucocorticoid administration, brood size manipulation, and immunological challenges have all been found to affect adult song and associated neurological structures (reveiwed in Buchanan et al., 2013; MacDougall-Shackleton & Spencer, 2012; Spencer & MacDougall-Shackleton, 2011). However, not all published experimental manipulations have found an effect of developmental stress on song learning. This is particularly evident in the multitude of zebra finch (Taeniopygia guttata) studies, where the effects of a variety of developmental stressors on song learning are inconsistent, with some studies reporting effects (Brumm, Zollinger, & Slater, 2009; de Kogel & Prijs, 1996; Holveck, Vieira de Castro, Lachlan, ten Cate, & Riebel, 2008; Spencer, Buchanan, Goldsmith, & Catchpole, 2003; Tschirren, Rutstein, Postma, Mariette, & Griffith, 2009; Zann & Cash, 2008), and others no effect (Gil, Naguib, Riebel, Rutstein, & Gahr, 2006; Kriengwatana et al., 2014).

The exact reasons for such discrepancies between studies, especially with zebra finches, remain unclear because the mechanisms by which developmental stress affects song have yet to be firmly established. Currently, developmental stressors are hypothesized to largely exert their effects by acting on the hypothalamic-pituitary-adrenal (HPA) axis. When stressors are perceived, the HPA axis mediates the physiological stress response by secreting glucocorticoids hormones (corticosterone [CORT] is the primary avian glucocorticoid). Thus, the way by which any type of developmental stressor affects song could be through activation of the HPA axis and the subsequent effects of CORT. For example, food restriction can alter baseline and stress-induced levels of CORT in birds (Kitaysky, Kitaiskaia, Wingfield, & Piatt, 2001; Pravosudov & Kitaysky, 2006) and elevated CORT due to food restriction can in turn adversely affect brain development (Welberg & Seckl, 2001). In support of this, some studies have found parallel effects of CORT and food restriction (Buchanan, Spencer, Goldsmith, & Catchpole, 2003; Schmidt, Moore, MacDougall-Shackleton, & MacDougall-Shackleton, 2013; Spencer et al., 2003).

Nevertheless, food restriction can also cause specific song and neural deficits not always seen in birds treated only with CORT. For example, song sparrows (Melsospiza melodia) that were either treated with CORT or food restricted both experienced a reduction in song complexity, but only food-restricted males were less accurate copying tutor song and had smaller volumes of the song-related brain nucleus RA (Schmidt, Moore, et al. 2013). This indicates that the influence of developmental stress on song may not necessarily be mediated by CORT per se, but by changing resource availability or resource allocation strategies of young birds. That is, birds that are deprived of nutrients early in life may have fewer substrates to allocate toward brain development (Welberg & Seckl, 2001), or may prioritize the development of certain systems when faced with limited nutrients (Schew & Ricklefs, 1998). These resource allocation strategies may, for instance, manifest as catch-up growth, which is a period of accelerated growth after a period of food restriction (Metcalfe & Monaghan, 2001). Catch-up growth may be beneficial in the short term by improving chances of survival, but there may be lifelong physiological debts incurred to paying the costs of this compensation (Metcalfe & Monaghan, 2001). Songbirds that are food restricted in early life often show slower growth rates, but typically these birds catch up to their control counterparts in adulthood once the stressor has been removed (Krause, Honarmand, Wetzel, & Naguib, 2009; Krause & Naguib, 2011; Kriengwatana, Wada, Macmillan, & MacDougall-Shackleton, 2013; Schmidt, MacDougall-Shackleton, & MacDougall-Shackleton, 2012; Spencer et al., 2003). Collectively, these studies suggest that glucocorticoid regulation is an important, but not the sole, mechanism by which developmental stress can have long-lasting impacts on birdsong. Glucocorticoid regulation may in fact be a component of resource allocation strategies that young birds employ when faced with stress during development (Wingfield et al., 1998).

The inconsistent effects of developmental stress on zebra finch song provide important clues about factors that can alter the impact of developmental stress on song. Specifically, the variety of developmental manipulations used across the studies to date (discussed in Kriengwatana et al., 2014), the relative importance of song in courtship displays, and relaxed sexual selection pressures due to domestication and variation among zebra finch colonies may contribute to these divergent findings. Increasing brood size and increasing foraging difficultly are two common methods of increasing developmental stress because of their ecological validity. However, it is difficult to identify the reasons why identical manipulations would yield different results (e.g., Brumm et al., 2009; Kriengwatana et al., 2014; Spencer et al., 2003; Zann & Cash, 2008) when researchers cannot quantify exactly how much stress is being applied to a nest and if all nestlings within a brood are experiencing the stressor equally. That is, it is logistically difficult to isolate zebra finch nestlings and strictly enforce a stressor if they are being fed by their parents and living in a brood. The fact that zebra finch song is part of a multimodal courtship display (i.e., females can see and hear males during courtship) suggests that their song may not be under as intense sexual selection as other species’ because song is one among many signals that zebra finch females use to assess potential mates (Bennett, Cuthill, Partridge, & Maier, 1996; Collins, Hubbard, & Houtman, 1994; Collins & ten Cate, 1996). In contrast to studies on zebra finches, data from free-living songbirds in which song clearly is and continues to be under intense sexual selection, such as European starlings (Sturnus vulgaris) and song sparrows (Melospiza melodia), have provided strong support for the developmental stress hypothesis (European starlings: Buchanan et al., 2003; Farrell, Weaver, An, & MacDougall-Shackleton, 2011; Spencer, Buchanan, Goldsmith, & Catchpole, 2004; song sparrows: MacDonald, Kempster, Zanette, & MacDougall-Shackleton, 2006; Schmidt, Moore, et al., 2013). Developmental stressors impair song learning and development of the song-control system in these species, and the size of song nucleus HVC is correlated with sexually selected components of song in free-living birds (starlings: Bernard, Eens, & Ball, 1996; song sparrows: Pfaff, Zanette, MacDougall-Shackleton, & MacDougall-Shackleton, 2007). Therefore, if song is a highly relevant indicator of fitness for a species, then we would expect that the mechanisms regulating song in that species are more susceptible to early environmental manipulations (Buchanan et al., 2013). Emphatically, developmental stress may not have the same consequences for fitness across all species or populations (i.e., colonies of domesticated birds) if song is not a primary metric by which females are evaluating potential mates.

Females and the Developmental Stress Hypothesis

As birdsong is the metric by which developmental stress has been assessed in most studies, little effort has been made to understand how stressors during development affect the receiver’s ability to perceive and respond to song. To our knowledge, there are no studies to date of the effects of developmental stress on a male’s ability to perceive and respond to songs during development or in adulthood (but for a manipulation of the early tutoring environment see Sturdy, Phillmore, Sartor, & Weisman, 2001). There are, however, a limited number of studies examining how developmental stress affects females’ responses to songs.

Most work on the effects of stress on females has been conducted with zebra finches. Data collected so far support the notion that females prefer the songs of control males to their stressed counterparts, and that female preferences may be altered by developmental stress (Riebel, Naguib, & Gil, 2009; Spencer et al., 2005). Female zebra finches prefer the songs of control males to previously stressed males, which suggests that developmental stress alters songs in a biologically relevant fashion (Spencer et al., 2005). In Riebel et al. (2009), female zebra finches raised in large broods (therefore presumed to have experienced more developmental stress) demonstrated equally strong preference for their tutor song as females from smaller broods. But, when given two songs from unfamiliar males singing a ‘short’ or ‘long’ song based on motif duration, females from small broods demonstrated stronger absolute preferences for one song type (i.e., there was no consistent directionality of the preference; Riebel et al., 2009). In similar studies, females were found to prefer males whose developmental background matched their own—in song preference tests and live interactive mate choice trials the females from small broods preferred males from small broods, and vice versa for large brood females (Holveck, Geberzahn, & Riebel, 2011; Holveck & Riebel, 2010). However, food restriction early in life did not affect female zebra finch preferences for song complexity when given song from unfamiliar singers, but did reduce overall activity during mate choice trials (Woodgate et al., 2011; Woodgate, Bennett, Leitner, Catchpole, & Buchanan, 2010). Overall, these studies indicate that developmental stress can have some influence over a female’s response toward song (i.e., less motor activity), but there is no compelling evidence to suggest that developmental stress alters a female’s preference toward song based on measures of song quality alone (i.e., complexity, motif duration). Be that as it may, studies that have found preference effects also have a potential confound—the similarity of the stimulus songs to songs a female would have been exposed to during the sensorimotor phases of vocal development are often not accounted for. Exposure to song in early life is a determining force to shaping female zebra finches’ song preferences (Lauay, Gerlach, Adkins-Regan, & DeVoogd, 2004; Riebel, 2000). A female raised in a large brood may acquire a preference for songs that share similar features to the songs of her development (i.e., similar to the song of her stressed father and/or developmentally stressed siblings). Therefore, rather than solely assessing if stress affects preferences for songs that vary in complexity, an additional consideration may be to assess preference for songs that vary features that sound more or less similar to the songs heard during the sensorimotor learning phase.

Apart from zebra finches, the only other species to date that has been studied with respect to the effects of developmental stress on female song preferences are song sparrows. Females that experienced food restriction or CORT treatment show reduced preferences for conspecific song (versus a heterospecific song) compared to control females (Schmidt, McCallum, MacDougall-Shackleton, & MacDougall-Shackleton, 2013). In addition, these stressed females showed patterns of neural activity in auditory forebrain areas (as measured by immunoreactivity of the immediate-early gene Zenk) that were not different when they listened to either conspecific or heterospecific song, while control females show significantly more immunoreactivity when listening to conspecific than heterospecific song (Schmidt, McCallum, et al., 2013). This study suggests that female preferences are condition dependent (Cotton, Small, & Pomiankowski, 2006) and may in part be caused by differences in neural activation in auditory forebrain regions in response to song (Schmidt, McCallum, et al., 2013). Unlike the zebra finches studies, all song sparrows in the Schmidt, McCallum, et al. (2013) study had the same exposure to tutor songs (a combination of live and tape tutors) in development, and all stimulus songs were from males whose repertoires were unfamiliar. Therefore, differences in early-tutoring environments are not likely the cause of the weaker preferences seen in song sparrows.

Even though female preference and mating decisions are swayed by the quality of male song (Gil & Gahr, 2002; Searcy, 1992), what exactly does a ‘good’ song advertise? Does song advertise direct benefits (e.g., increased parental feeding through superior foraging abilities), indirect benefits (e.g., good genes), or both? Moreover, what characteristics of song are most constrained by early developmental stress, and are they the same characteristics that females are using to evaluate prospective mates? Assessing preferences, and how developmental stress may alter them, should be tailored to each species due to ecological and life history characteristics that differ between them. In some species, specific content within a song is important for preference, such as sharing songs, singing a local dialect, or singing specific syllables (e.g., fast trills in canaries; Gil & Gahr, 2002). However, the reasons why females prefer these song characteristics could be established through different mechanisms (Searcy & Andersson, 1986), and therefore developmental stress may affect some species more selectively. For example, zebra finch mate preferences are strongly affected by parental imprinting such that zebra finches raised by Bengalese finches (Lonchura striata domestica) will almost exclusively display sexually in adulthood to Bengalese finches rather than conspecifics (ten Cate & Vos, 1999). Yet, female house finches (Carpodacus mexicanus) tutored by a foreign dialect, or in complete isolation, still preferred song from their local dialect in adulthood, despite no previous experience with it, which suggests some song preferences may be innate (Hernandez & MacDougall-Shackleton, 2004). Species for whom early auditory experiences are instrumental to shaping song preferences may be more susceptible to developmental stress than those whose preferences are less dependent on auditory experience.

Studies in wild bird populations generally support the developmental stress hypothesis that birdsong evolved as an honest signal of the developmental history of the singer. Attention is now turning toward understanding how developmental stress affects the perception of song. Currently, it appears that developmental stress may alter how females respond to songs, and preferences in some instances, but no tests so far have convincingly shown that these results are caused by developmental stress impairing mechanisms of perception. This, along with other questions (e.g., Does developmental stress selectively affect song perception? What are the neural bases for such perceptual impairments? And, if perceptual abilities were compromised, what would be the consequences to an individual’s fitness?), will need to be addressed in order to fully understand how early developmental factors can influence the coevolution of production and perception of increasingly elaborate sexually selected signals.

Developmental Stress and Correlated Cognitive Traits: Are the Best Singers Also the Smartest?

Early-life environments are fundamental to shaping an organism’s phenotype, exerting effects on a multitude of physiological and cognitive-behavioral traits (Metcalfe & Monaghan, 2001). In songbirds, physiological effects of developmental stress include altered growth rates, body size, organ mass, and immune and metabolic functioning (Kriengwatana et al., 2013; Verhulst, Holveck, & Riebel, 2006). Developmental stress can also affect other cognitive and behavioral processes in addition to affecting song, such as associative learning in zebra finches (Fisher, Nager, & Monaghan, 2006; Kriengwatana, Farrell, Aitken, Garcia, & MacDougall-Shackleton, in press) and spatial learning in Western scrub-jays (see the Developmental Stress and Spatial Cognition section for a detailed discussion; Pravosudov, Lavenex, & Omanska, 2005). Importantly, song characteristics appear to be correlated with a number of physiological and cognitive measures. For instance, song is correlated with immune function, body condition, endocrine function, survival, and fitness in song sparrows (Hasselquist, Bensch, & von Schantz, 1996; MacDougall-Shackleton et al., 2009; Pfaff et al., 2007; Schmidt et al., 2012; Schmidt, MacDougall-Shackleton, Soma, & MacDougall-Shackleton, 2014), inhibitory control in song sparrows (Boogert, Anderson, Peters, Searcy, & Nowicki, 2011), problem solving in zebra finches (Boogert, Giraldeau, & Lefebvre, 2008), and spatial learning in European starlings (Farrell et al., 2011). Consequently, these correlations suggest that if song is an honest indicator of early-life conditions (as posited by the developmental stress hypothesis), then song could also be an honest indicator of the quality of other cognitive functions that develop in parallel with song. In this section, we investigate the extent to which song is predictive of other cognitive functions, examine how developmental stress may explain the persistence of the relationships between song and other cognitive functions even after the stressor has been removed, and draw attention to the importance of understanding whether the effects of developmental stress on cognition have significant fitness consequences.

It is only recently that researchers began to test the relatively simple idea that the best singers are also the smartest, even though it seems logical to assume that the neural processing required for song learning may be correlated to other cognitive functions (Catchpole, 1996; Nowicki & Searcy, 2011). Numerous neural systems develop in tandem, and correlations can arise if they have overlapping critical sensitive periods and resource requirements (Buchanan et al., 2013; Spencer & MacDougall-Shackleton, 2011). For instance, the neural systems that regulate song learning and spatial memory (song-control system and hippocampus, respectively) are functionally independent in the developing zebra finch (Bailey, Wade, & Saldanha, 2009). Yet, these systems have developmental schedules that likely overlap (Brainard & Doupe, 2002; Clayton, 1996) and therefore could both be simultaneously affected by stressful environmental factors. If both systems are shaped by the same environmental factors (such as stressful rearing environments), this could result in them being correlated in adulthood despite their functional independence (Nowicki et al., 1998). Farrell et al. (2011) illustrate such an association: starlings that experienced a nutritional stressor in early development were impaired on a spatial memory task and scored lower on a measure of song quality. Moreover, the starlings that were better at the spatial foraging task also went on to sing more complex songs in their first breeding season (Figure 2).

Figure 2. Developmental stress affected both spatial and song abilities in starlings (figures modified with permission from Farrell et al., 2011). (A) An overhead view of the spatial foraging arena used in the spatial memory task. Birds were tested daily for 4 weeks with the same formation of 4 of 16 cups baited with mealworms. Cups were covered with tissue paper so birds had to peck through the paper to obtain the worm. (B) Performance as measured by number of incorrect cups searched across the 4-week testing phase of the spatial memory task. Controls (blue line) birds made significantly fewer errors than the food-restricted birds (orange line) across the 4-week testing period. (C) A male starling from the study singing in an aviary. (D) Average song bout length for both the control and food-restricted males from the study. Males raised in control conditions sang significantly longer song bouts in an undirected singing situation than males raised in a food-restricted condition. (E) Males that sang longer song bouts made fewer errors on the spatial memory task during the first 2 weeks of testing. Each point represents a male starling from the study coded by its early developmental condition (blue circle: control male, orange triangle: food-restricted male), but the regression line is based on all birds.

However, song is not always predictive of other cognitive traits, because for every positive correlation there have been an almost equal number of null, or even negative, correlations between song quality and other cognitive traits. In the aforementioned study with song sparrows (Boogert, Anderson, et al., 2011), there was no significant relationship between repertoire size and performance on a color association task or a reversal task. Templeton, Laland, and Boogert (2014) tested flocks of zebra finches on the same problem-solving task as Boogert et al. (2008) but did not replicate Boogert et al.’s results showing a positive relationship between song complexity and problem-solving performance in zebra finches tested in isolation. Specifically, Templeton et al.’s (2014) study did not find any relationship between song complexity of solvers and non-solvers, nor between song complexity and latency to solve the task among solvers. The researchers cite responsiveness to social isolation as the reason underlying the correlation previously reported by Boogert et al. (2008). A separate study in song sparrows found a negative relationship between spatial memory and song repertoire size (Sewall, Soha, Peters, & Nowicki, 2013), which the authors suggest could be the result of a trade-off between the development of song and spatial cognitive systems.

What might explain why song is predictive of other cognitive functions in some studies but not others? One explanation is that song correlates only with performance on particular cognitive tests. However, not all of the above outlined tests have been validated to assess specific cognitive processes (Thornton, Isden, & Madden, 2014). Alternatively, developmental history (i.e., the amount of developmental stress experienced) may contribute to differences in experience, motivation, and other factors that contribute to performance on cognitive tests (reviewed in Thornton & Lukas, 2012). Unfortunately, the developmental history of individuals is not always reported, making comparisons across studies rather difficult. Developmental history must be considered if we are to understand the importance of developmental stressors in organizing correlations among adult cognitive and behavioral traits—in this case, among song learning and other types of learning abilities. In light of these confounds, we strongly advise researchers interested in knowing how song is linked to other cognitive processes to use more rigorous testing and established experimental protocols (i.e., tests that have been validated to assess specific cognitive processes), and to always take note and report the developmental history of experimental subjects. To make the claim that song quality is predictive of other cognitive abilities, the onus must be placed on experimenters to demonstrate that these correlations are due to condition-dependent effects on the systems in question (Buchanan et al., 2013).

The relationship between song and other cognitive processes is predicted to be positive if the development of neural substrates mediating song and cognition overlap in time and if developmental stressors similarly affect song and cognition. Null or negative correlations between song and other cognitive traits could be explained if the neural substrates supporting a particular cognitive ability are developed at different times than the song-control system, or if the development of neural/physiological systems that maintain more essential functions than song are canalized, or buffered from developmental stressors. A third (and often implicit) assumption made when predicting the relationship between song and other cognitive functions is that they are, at least partly, mediated by common learning processes.

If song learning really does signal general learning abilities, this implies that there are common underlying processes for song and other forms of cognition, that is, a general intelligence factor in songbirds. General intelligence is a construct that captures cognitive performance across a series of tasks and suggests that performance on one task may be reflective of performance on other tasks (Deary, Penke, & Johnson, 2010). To date, there is evidence for general intelligence abilities in several species (reviewed in Boogert, Fawcett, & Lefebvre, 2011; Thornton & Lukas, 2012). Large-scale cognitive testing in birds is in its infancy, but we turn to recent studies on whether the elaborate bower-building displays (i.e., structures made of sticks and objects) of bowerbirds are reflective of a general intelligence factor (Isden, Panayi, Dingle, & Madden, 2013; Keagy, Savard, & Borgia, 2011a; Keagy, Savard, & Borgia, 2011b). Overall, there are few direct correlations between performances on different cognitive tasks in male satin bowerbirds (Ptilonorhynchus violaceus), but there is some evidence for a general intelligence factor, calculated based on shared covariance between the tests using principal components analysis (i.e., SB-g; Keagy et al., 2011b). However, in this species measures of song quality did not correlate with individual problem-solving abilities (Keagy et al., 2011b). In a congeneric species, spotted bowerbirds (Ptilonorhynchus nuchalis), one factor explains 44% of the covariance of test performance across a battery of tests unrelated to bower building (unlike tasks performed by Keagy, Savard, & Borgia, 2009, 2011a, 2011b). However, none of the individual tests or this aggregate score correlated with mating success (but see Isden et al., 2013). To date, the evidence from bowerbirds does not support the hypothesis that song quality is related to a general intelligence factor extracted from performance across various cognitive tests. Female bowerbirds appear to be making mating decisions on both measures of song and bower building in these species, which suggests that these traits are likely advertising different information (Candolin, 2003). Therefore, any general intelligence factors that are extracted from these studies likely reflect cognitive processes more or less independent of those necessary for song learning and performance.

Cognition and Fitness

Although there is good evidence that females of at least some songbird species preferentially choose males based on song (e.g., Hasselquist et al., 1996; Searcy, 1992), there is limited evidence that females choose males directly based on cognitive abilities (Boogert, Fawcett, et al., 2011). For example, female red-crossbills (Loxia curvirostra) observed males extracting seeds from pinecones and displayed a preference for the males that were faster at the task (Snowberg & Benkman, 2009), but female zebra finches showed no preference for males’ foraging technique (Boogert, Bui, Howarth, Giraldeau, & Lefebvre, 2010). Future research is warranted to determine whether females use song to assess a male’s cognitive abilities, or if they are also directly assessing other cognitive behaviors in mate choice situations. However, if a female chooses to mate with a “smarter” male it may not ultimately matter whether her decision was based directly on cognition or not, as long as she receives the fitness benefits (direct and/or indirect) from mating with a “smarter” male (Boogert, Fawcett, et al., 2011).

Song is a trait on which females appear to base mate choice decisions and this mate choice is hypothesized to result in fitness gains if that male also has greater cognitive abilities. However, rarely is the link between song and cognitive ability studied within the context of fitness. In the previous sections, we have reviewed the tenuous relationship between song and other cognitive domains. Most of the cognitive tests used are artificial ones designed by experimenters, making it difficult to determine whether performance on such tests is related to fitness in a natural setting. A behavior that is deemed “smart” by an experimenter may not yield an increase in overall fitness if: (a) the task is far removed from a biologically relevant context, (b) there are alternative strategies that are equally or more profitable (e.g., foraging versus scrounging), or (c) displaying such a behavior would not add, or would be counterproductive, to an animal’s reproductive success/fitness. Ultimately, “smart” behaviors evolve because they increase an individual’s survivability and/or reproductive success. And yet, how particular cognitive phenotypes benefit an animal’s fitness is a question that often falls outside the scope of most experiments (Healy, 2012).

Intelligence is assumed to be beneficial, but very few studies have found a direct link between cognition and fitness. In fact, researchers often do not consider the costs that may result from being smart. In one study that did so, the problem-solving abilities of over 400 great tits (Parus major) were related to their reproductive success (Cole, Morand-Ferron, Hinks & Quinn, 2012). Female tits briefly tested in captivity that were successful at a problem-solving task went on to have larger clutches and fledged more young. These fitness gains did not come at the expense of the mother’s own condition, but rather because these females had significantly smaller foraging areas. This implies that successful solvers were better able to exploit their habitat when foraging and therefore spent less time away from the nest, which in turn meant that they could engage in more nest-attentive behaviors that would increase the odds that young successfully fledged (Cole et al., 2012). But, successful solvers were also more likely to desert their nest after a nest disruption. Solvers in this population are known to have a heightened startle response and are less competitive in social situations (Cole & Quinn, 2012; Dunn, Cole, & Quinn, 2011); thus problem-solving appears to have both costs and benefits in this species.

Together, the above findings suggest that in this population of great tits there could be two alternative life-history strategies, with each strategy resulting in higher fitness benefits under particular circumstances. Solvers appear to be more reactive to nest disruptions and therefore could fledge fewer young compared to non-solvers in a season where nest disturbance events are high. Conversely, if food was not as abundant, solvers may fledge more young because they are able to exploit their habitats more efficiently than non-solvers. Therefore, cognitive ability may only be employed in situations where it is a rewarding strategy. If there is a genetic basis to these cognitive and personality phenotypes, then individuals may be under different selection pressures based upon ecological constraints (Svanbäck & Bolnick, 2007).

Developmental Stress and Spatial Cognition

In mammals, the association between developmental stress and deficits in spatial cognition in adulthood is well researched (Lupien, McEwen, Gunnar, & Heim, 2009; Vallée et al., 1999; Yang, Han, Cao, Li, & Xu, 2006). While studies of spatial cognition are prominent in the songbird literature, few have yet to manipulate developmental conditions and observe the effects on spatial cognition and the hippocampus. In this section, we review the few studies that have conducted such manipulations, remarking on a potential mechanism by which developmental stress could affect spatial cognition, and highlight future avenues of research.

For food-caching species, spatial cognition is a conspicuous trait that is closely linked to survival (Pravosudov & Lucas, 2001). Western scrub-jays (Aphelocoma claifornica) are prolific food-cachers—when food is plentiful, they store food in hidden locations and will retrieve these items in times of food scarcity. Pravosudov et al. (2005) hypothesized that stressful conditions in early development could induce cognitive deficits in scrub-jays, which would become evident when they performed tasks as adults that relied on spatial ability. This hypothesis was supported by their findings, as jays that experienced nutritional restriction in early life performed more poorly than control birds on tasks assessing cache recovery and spatial-association learning. Furthermore, when given conflicting spatial/color information to locate a food reward, the jays overwhelmingly preferred to solve the task based on the color information compared to birds that did not experience nutritional restriction. In the same birds, nutritional restriction impeded growth of the hippocampus, as food-restricted jays had smaller hippocampal volume and fewer hippocampal neurons, even though overall telencephalon volume and brain mass were unaffected. As memory for spatial location and memory for color appear to be mediated by separate mechanisms (Hampton & Shettleworth, 1996; Sherry & Vaccarino, 1989), Pravosudov et al.’s (2005) results reveal that the effects of stress in the brain are not uniform, and that some neural structures may be more sensitive to the effects of early nutritional stress than others.

For non-caching species, spatial cognition is involved in a variety of behaviors (e.g., migration, orientation, exploration; Shettleworth, 2009). In two non-food-caching species, manipulating diets early in life also had negative effects on performance in spatial memory tasks. Arnold, Ramsay, Donaldson, and Adam (2007) manipulated levels of taurine, an amino acid implicated in brain development and the regulation of the stress response (Engelmann, Landgraf, & Wotjak, 2003; Lapin, 2003), in nestling blue tits (Cyanistes caruleus). When tested as juveniles, blue tit females that had been supplemented with taurine showed a trend toward committing fewer errors on a spatial reference memory task. Similarly, starlings that were subjected to an unpredictable food supply during the juvenile phase committed more reference and working memory errors on a spatial foraging task compared to starlings raised on an ad libitum diet (Farrell et al., 2011; Figure 2B). Zebra finches subjected to nutritional stress before nutritional independence also performed more poorly in a hippocampus-dependent spatial memory task (Kriengwatana et al., in press). Although spatial performance was affected by early developmental treatments, neither of the aforementioned studies assessed the hippocampus or any other neural structures.

Developmental stress exists in many forms (e.g., brood enlargement, nutritional deficits, unpredictable environments, parasites, and infectious diseases), yet the resulting effects on the physiology of the song-control system and the hippocampus are similar: smaller volumes and fewer neurons. Therefore, the hippocampal differences in jays reported by Pravosudov et al. (2005) may not be due to a nutritional deficit per se, but rather the result of a physiological mechanism that, when stimulated by environmental stress, inhibits neural development. As discussed previously, a likely mechanism is the HPA axis, which regulates the physiological stress response involving glucocorticoids (CORT). Glucocorticoids may have short-term activational and long-term organizational effects on the hippocampus because this brain region is rich in two receptors (mineralocorticoid receptors, MR, and glucocorticoid receptors, GR) that regulate negative feedback and thus mediate the effects of glucocorticoids (Liu et al., 1997). Stressful early conditions increase circulating glucocorticoids and alter levels of glucocorticoid receptors in the hippocampus, which may subsequently lead to spatial memory deficits (Banerjee, Arterbery, Fergus, & Adkins-Regan, 2012; Hodgson et al., 2007; Pravosudov & Kitaysky, 2006). In zebra finches, offspring that were deprived of maternal care had a more exaggerated stress response to social isolation and fewer MR were observed across the brain, including within the hippocampus (Banerjee et al., 2012). Similarly, a selectively bred line of zebra finches that had high CORT in response to acute stress were found to have worse performance on a spatial memory task and fewer MR within the hippocampus (Hodgson et al., 2007). MR is thought to preserve neuronal integrity and excitatory tone within the hippocampus, and therefore a decrease in their number could compromise cognitive processes specific to the hippocampus (Joëls, Karst, DeRijk, & de Kloet, 2008; Joëls, 2008).

Food-caching birds provide a fruitful model for future work, as there are many established experimental protocols for assessing a variety of aspects of spatial memory and its relation to the hippocampus. Clayton and colleagues have studied the various ways scrub-jays (Aphelocoma coerulescens) and western scrub-jays cache their food and convincingly demonstrate that these birds integrate temporal, contextual, and social information in memory (Clayton, Dally, & Emery, 2007; Clayton & Dickinson, 1998). We know that developmental stress impairs hippocampus development and spatial memory in this species, but how might such stress affect temporal, semantic, and social memory? These and other cognitive processes, such as emotional processing and memory formation, may also depend on hippocampus function (Eichenbaum, 1996). In addition, while the volume of the hippocampus may reflect spatial performance, there could be additional aspects aside from volume that contribute to spatial and other forms of cognition. Hippocampal volume is a proxy of spatial cognition, as variables such as age, captivity, and seasonal effects can alter hippocampal volume (Roth, Brodin, Smulders, LaDage, & Pravosudov, 2010). Examining small-scale changes, such as the integration of newly generated neurons within the hippocampus, may be a more sensitive measure that reflects variation within spatial memory ability. Future research examining the effects of developmental stress should include variables that capture neuron integration, such as neuron proliferation, neuron type/size, glial counts/size, dendritic branching, and length of branching (Roth, Brodin, et al., 2010). Moreover, examining differences in gene expression for MR and GR receptors may be of importance as the hippocampus is an extrahypothalamic site for negative feedback of the HPA axis (Welberg & Seckl, 2001). Differences in these small-scale measures may not be reflected in the larger-scale measure of hippocampal volume and will further our understanding of how developmental stress may affect the brain. Developmental stress affects hippocampal development and spatial cognition, but future efforts are required both to understand the neural mechanisms behind these effects and to clarify how other memory systems may be affected.

Personality/Behavioral Syndromes

Behavioral syndromes can be defined as “a suite of correlated behaviors reflecting between-individual consistency in behavior across multiple (two or more) observations” (Sih & Bell, 2008, p. 231). Correlated behaviors should maintain a consistent and stable relationship, which is to say they should not change in the face of transient factors (e.g., motivation), but can change based on life history stages and social contexts (Groothuis & Carere, 2005; Schuett & Dall, 2009; van Oers, 2005). The definition of behavioral syndromes is inclusive and subsumes other similar, but not synonymous terms, such as coping styles, personality, and temperament (Sih & Bell, 2008; Stamps & Groothuis, 2010a). Consequently, we consider studies using any of the aforementioned terms as studies of behavioral syndromes.

The suite of correlated traits that comprise behavioral syndromes in songbirds is currently not well understood. So far, the most comprehensive studies of avian behavioral syndromes have been undertaken in great tits (Parus major). However, we must be cautious about assuming that these findings are applicable to other birds. In great tits, the terms reactive and proactive have been used to describe birds that exhibit slow exploration of novel environments and increased latency to investigate a novel object, and fast exploration of a novel environment and reduced latency to investigate a novel object, respectively (reviewed in Groothuis & Carere, 2005). Compared to reactive birds, proactive birds are consistently more aggressive, socially dominant, less behaviorally flexible, and secrete less CORT in response to social and restraint stress (Baugh et al., 2012; Carere, Drent, Privitera, Koolhaas, & Groothuis, 2005; Carere, Groothuis, Möstl, Daan, & Koolhaas, 2003; van Oers, Drent, de Goede, & van Noordwijk, 2004; Verbeek, Boon, & Drent, 1996; Verbeek, Drent, & Wiepkema, 1994). These different personality types can be influenced by genetic and nongenetic factors (Carere, Drent, Koolhaas, & Groothuis, 2005; Drent, van Oers, & van Noordwijk, 2003; Stamps & Groothuis, 2010b; van Oers et al., 2004). Studies on birds such as zebra finches and black-capped chickadees (Poecile atricapillus) also find consistent individual differences in exploratory behaviors (An, Kriengwatana, Newman, & MacDougall-Shackleton, 2011; Beauchamp, 2000; David, Auclair, & Cézilly, 2011; Krause & Naguib, 2011; Schuett & Dall, 2009). In zebra finches, selection for physiological responsiveness to stress is also associated with differences in exploration. Specifically, in zebra finch lines artificially selected for low and high responses to acute restraint stress, greater exploratory behavior was linked to higher CORT only in the low CORT line (Martins, Roberts, Giblin, Huxham, & Evans, 2007). While the work in great tits has been extremely influential for understanding avian behavioral syndromes, we must be careful when generalizing across species because relationships between traits may not exist or may be different in other species. For example, exploration and boldness were not correlated in zebra finches or in a non-songbird (Japanese quail; Coturnix japonica; Martins et al., 2007; Zimmer, Boogert, & Spencer, 2013), and learning an acoustic discrimination task was positively correlated with exploratory behavior in black-capped chickadees but not in great tits (Groothuis & Carere, 2005; Guillette, Reddon, Hurd, & Sturdy, 2009).

Few studies in birds have investigated the impact of developmental experiences on behavioral syndromes, although their importance to behavioral syndromes is gaining recognition (Groothuis & Trillmich, 2011; Stamps & Groothuis, 2010a, 2010b; Trillmich & Hudson, 2011). As explained in detail earlier, stressors during development can affect diverse physiological and behavioral traits and consequently may influence behavioral syndromes if they can affect the strength and direction of the correlation between these traits (Spencer & MacDougall-Shackleton, 2011). Data from the studies available do not yet clearly establish how developmental stress affects behavioral syndromes. Among others, one important variable that is inconsistent between studies is the timing of developmental stress and the time at which behaviors are assessed. The timing of stress during development is an important source of variation in offspring behavior, with both prenatal and postnatal stress (as well as the time within those stages) having potentially different behavioral outcomes (Boogert, Zimmer, & Spencer, 2013; Henriksen, Rettenbacher, & Groothuis, 2011; Krause et al., 2009; Kriengwatana, 2013; but see Zimmer et al., 2013). Furthermore, the time at which behaviors are assessed is also important because behavioral variation between individuals may change across the lifespan, thus making it difficult to detect covariation (Sih & Bell, 2008). Below we discuss separately the studies that investigate prenatal and postnatal stress.

Prenatal Stress

Prenatal stress is assumed to affect offspring behaviors through altering maternal steroid hormone deposition in eggs, decreasing maternal investment during egg formation, or affecting maternal care behaviors such as incubation. In a comprehensive review of prenatal stress in birds, Henriksen et al. (2011) noted that maternal stress (via CORT injections in the female or unpredictability of feeding) reduced offspring competitiveness, but that this result was not always observed if CORT was injected directly into the eggs. The effects of CORT injections into eggs on fearfulness and anxiety are mixed, and it is not clear whether these measures can be treated as measures of boldness and exploration, or whether these measures showed individual consistency and inter-individual variation and can thus be deemed as components of a behavioral syndrome (for a discussion regarding fearfulness as an aspect of behavioral syndromes see Cockrem, 2007). One study in Japanese quail that injected CORT into eggs and measured both boldness and exploration found that prenatal stress increased exploration but not boldness (Zimmer et al., 2013). Moreover, birds in this study that received both prenatal and postnatal stress treatments tended to be the most explorative and risk taking compared to birds that received pre- or postnatal stress treatments or control treatment. This suggests a cumulative effect of pre- and postnatal stress on measures of behavioral syndromes. Nevertheless, more studies are needed before generalizations can be made about the effects of prenatal stress on behavioral syndromes. In addition to manipulating CORT, future studies should also consider whether incubation temperature could alter offspring behavior, as previous work shows that it is able to affect a variety of physiological measures (Henriksen et al., 2011). In addition, it will be important to explore variation between species that produce altricial versus precocial young, as the physiological systems that develop prenatally in ovo will markedly differ. Recent work from our lab (H. Wada, unpublished) found that manipulations of incubation temperature in zebra finches (that produce altricial young) had very different effects than those reported for wood ducks (that produce precocial young; Durant, Hepp, Moore, Hopkins, & Hopkins, 2010). Thus, the distinction between pre- and postnatal manipulations will vary for species with different developmental schedules.

Postnatal Stress

Sources of postnatal stress include food availability, sibling competition, parental favoritism, and disease/parasitism. Investigations of the effect of early postnatal stress on components of behavioral syndromes are shown in Table 1. The strongest evidence that developmental stress can affect behavioral syndromes comes from a study that found that reducing food intake of great tit nestlings increased exploration and boldness (Carere, Drent, Koolhaas, et al., 2005). Importantly, food rationing increased aggression in great tits artificially selected to be fast explorers, indicating that postnatal developmental stress was able to alter the relationship between behavioral traits that are part of a well-established behavioral syndrome. However increased boldness and exploration were also observed in two other studies where birds may have experienced less developmental stress. Naguib, Flörcke, and van Oers (2011) found that great tits that experienced less sibling competition were faster to investigate novel environments and objects. Arnold et al. (2007) also found that blue tits (Cyanistes caeruleus) were bolder if they had been supplemented with taurine (an inhibitor of HPA axis activity; Engelmann et al., 2003) as nestlings. The discrepancy between the studies above may be due to the different manipulations used to alter developmental conditions. More research is needed to clarify how different stressors may produce different effects on personality traits.

In zebra finches there are methodological, age, and sex-specific effects on the development of behavioral phenotypes. Administration of postnatal CORT has had contradictory effects, with one study reporting decreases in boldness (males only) and competitiveness (both sexes; CORT administered PHD 7–18: Spencer & Verhulst, 2007), and another reporting no change in boldness in either sex (CORT administered PHD 12–28; Donaldson, 2009). However, diet manipulations during a similar developmental time period generated different results. Krause et al. (2009) found that reducing diet quality increased exploration (females only; diet manipulation from PHD 1–17); however, the same manipulation for a longer duration had no effect on exploration on males or females (Krause & Naguib, 2014; PHD 3–35). Similarly, Donaldson (2009) found no effect of reducing dietary protein on boldness in either sex. Instead, Donaldson (2009) reported that inconsistency of treatment (e.g., a switch from high to low protein diets and vice versa) rather than the diet itself decreased boldness in both sexes, although this result was nonsignificant (p = 0.052). This suggests that environmental instability, rather than stressful early environments per se, may mediate the effects of developmental stress on personality. This hypothesis has received mixed support. In support, Krause and Naguib (2011) found that accelerated catch-up growth resulting from alleviation of nutritional stress was negatively correlated with exploration in males and females, but early nutritional stress itself did not affect exploration. In opposition, Kriengwatana et al. (in press) reported no effect of nutritional stress (via food accessibility) or constancy of nutritional conditions between PHD 5–61 on boldness in adult birds.

Despite some conflicting results, the studies above raise three important observations. First, findings that postnatal stress can decrease boldness (Spencer & Verhulst, 2007) and increase exploration (Krause et al., 2009) suggest that boldness and exploration may not be correlated in zebra finches (Martins et al., 2007). Alternatively, postnatal stress may not have affected both boldness and exploration because of the different type of stress experienced (i.e., CORT administration versus nutritional stress). An experiment that manipulates postnatal stress and measures both boldness and exploration in the same individuals is needed to evaluate these possibilities. Second, different types of stress during development interact with sex to differentially affect exploration and boldness in males and females. Males may be very sensitive to increased CORT during the first days post-hatch (Spencer & Verhulst, 2007), whereas both males and females may be similarly sensitive to unavailability of dietary protein before they reach nutritional independence (around PHD 35; Donaldson, 2009). Third, these results highlight that the effects of developmental stress on behavioral phenotypes is contingent on the age or life stage at which behaviors are assessed. Boldness or exploration are consistent in zebra finches if the tests are repeated within the same day, the next day, or the following week (David et al., 2011; Krause & Naguib, 2011; Schuett & Dall, 2009), yet over the long term these traits may change (Donaldson, 2009). The lack of correlations between behaviors at different life stages may reflect less behavioral variability of a species in general at a certain age, or be caused by testing for behaviors in contexts that do not sufficiently reveal underlying inter-individual variability (Sih & Bell, 2008).

In summary, both prenatal and postnatal developmental stress can alter behaviors that constitute behavioral syndromes, but further investigation is necessary to determine whether stress can change the correlations between traits. Further studies are also required to determine whether the influence of developmental stress on behavioral syndromes is limited to early life—the only study that manipulated stress after nutritional independence found that it had no effect on boldness (Kriengwatana et al., in press). Another aspect that warrants further investigation is how developmental stress differentially affects behavioral syndromes in males and females. Developmental stress may have sex-specific effects because different sexes may respond to stress differently according to their life history strategies, and already there is some evidence of developmental stress producing sex differences in boldness and/or exploration (e.g., Arnold et al., 2007; Donaldson, 2009; Spencer & Verhulst, 2007). As males seem to be more consistent in exploration and boldness compared to females (Donaldson, 2009; Schuett & Dall, 2009), this indicates that females may be more behaviorally plastic than males. Last, because developmental stress can have such diverse effects, it would be beneficial to assess the relationship of boldness, exploration, and aggression with other behaviors that may be affected by stress, such as begging rates, song, and learning ability (Arnold et al., 2007; Brust, Krüger, Naguib, & Krause, 2014; Carere, Drent, Koolhaas, et al., 2005; Garamszegi, Eens, & Török, 2008; Groothuis & Carere, 2005). Studies in this direction would address how developmental experiences, by altering behavioral tendencies, can affect behavioral plasticity.

Conclusion

Here we have reviewed the evidence to date for the developmental stress hypothesis and how we can extend its underlying principles to the study of other cognitive traits and behavioral measures. Cognitive and behavioral traits that are influenced by environmental conditions could be signaled through song quality, conveying information to listeners about how well an individual coped with stressful early-life conditions and/or their heritable developmental stability. Although there has been much borne out of the developmental stress hypothesis, there are still areas where current research falls short and there are areas where concentrated efforts are still needed.

The predictions of the developmental stress hypothesis have been supported in multiple species using a variety of manipulations. However, not all manipulations have yielded the same effects on song (reviewed in Spencer & MacDougall-Shackleton, 2011), and therefore more research is needed to understand the mechanisms by which developmental stress operates. As we alluded to throughout the review, CORT is a likely candidate by which stress affects the brain. Recent research has found that there are receptors for corticosterone in the song-control system (Suzuki, Matsunaga, Kobayashi, & Okanoya, 2011), and therefore corticosterone is a likely vehicle by which stress alters song learning. Still, many questions remain unanswered. For instance, how does CORT affect cellular processing, neuronal migration, or connectivity between neural circuits? And do these CORT-induced neural changes lead to observable behavioral changes in song? Another important factor to consider are sex-specific differences with regard to developmental stress. Although it is the male of the species that typically sings, how stress affects females’ perception, preference, and choice for male song is a crucial component of the evolutionary equation. A better understanding of what benefits are bestowed upon a female when she chooses a male will go a long way to understanding the evolution of this sexually selected cognitive trait.

We emphasize that most work to date regarding the correlation across cognitive traits and birdsong is equivocal. However, the field is still in its infancy and future studies should strive to use validated psychometric tests, adequate sample sizes, and knowledge about developmental history of its subjects. It is easier to assess cognitive abilities with localized neural structures, such as spatial cognition and the hippocampus. As reviewed above, there are many excellent songbird models that study hippocampal functioning that are rich sources for future studies linking song and with various aspects of memory. Designing future experiments around tasks that assess known cognitive processes and underlying neural structures is a necessary step to further our understanding between song and specific forms of cognition. For example, consider the arcopallium, a region homologous to the mammalian amygdala (Abellán, Legaz, Vernier, Rétaux, & Medina, 2009) that regulates fear learning and is sensitive to CORT (Brown, Woolston, & Frol, 2008; Cohen, 1975). Differences in the volume of the arcopallium between two black-capped chickadee populations could potentially explain the differences in problem-solving abilities and neophobia responses also observed between these two populations (Roth, Gallagher, LaDage, & Pravosudov, 2012; Roth, LaDage, & Pravosudov, 2010). Examining how developmental stress may affect the functioning of this area, and subsequent associative fear learning, could be one example of a future study assessing behavior with known neural correlates.

Although we have discussed personality and cognitive traits separately, they should not be thought of as independent of each other. It is apparent that early developmental conditions can shape an organism’s phenotype, but more work is needed to understand how such changes could give rise to persistent behavioral strategies across a variety of contexts. The timing of developmental stress, and the temporal overlap between periods when traits are most sensitive to environmental influences are key factors that could explain the relationship between personality and cognitive abilities.

In conclusion, to understand cognition and the relationship among various cognitive traits, it is imperative that we have knowledge of an individual’s developmental history. This is because developmental events, especially stressful ones, can have persistent effects on the function of various cognitive traits that carry over into adulthood. The developmental stress hypothesis provides a powerful framework to synthesize findings across the fields of developmental and cognitive research. While the hypothesis focuses on explaining variation within birdsong, its central tenets can be applied to other aspects of an individual’s condition, and the principle in general can be applied to other animals’ systems.

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Two Fields Are Better Than One: Developmental and Comparative Perspectives On Understanding Spatial Reorientation

Alexandra D. Twyman
University of Western Ontario

Daniele Nardi
Sapienza University

Nora S. Newcombe
Temple University

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Abstract: Occasionally, we lose track of our position in the world, and must re-establish where we are located in order to function. This process has been termed the ability to reorient and was first studied by Ken Cheng in 1986. Reorientation research has revealed some powerful cross-species commonalities. It has also engaged the question of human uniqueness because it has been claimed that human adults reorient differently from other species, or from young human children, in a fashion grounded in the distinctive combinatorial power of human language. In this chapter, we consider the phenomenon of reorientation in comparative perspective, both to evaluate specific claims regarding commonalities and differences in spatial navigation, and also to illustrate, more generally, how comparative cognition research and research in human cognitive development have deep mutual relevance.

Keywords: spatial reorientation, geometric module, adaptive combination, individual differences, sex differences, slope

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One of the many unique characteristics of the human species is, arguably, the urge to reflect on what characteristics make us unique. There are many distinctive characteristics to consider, such as large brains, bipedal gait, lengthy childhoods, tool invention and use, symbolic representation and grammatically-structured language. But at least as interesting a question as what makes our species distinctive is the question of what we share with other species. In fact, systematic understanding of similarities as well as differences is arguably helpful to answering questions about species-uniqueness.

When we pursue a serious comparative cognition research strategy of this kind, the ability to navigate successfully is a central domain in which to work. Navigation is a crucial skill for all mobile organisms. Do all species use the sametechniques to navigate successfully? Common mechanisms could arise either because the essential problem was solved long ago by a common ancestor, or because the structure of the problem itself places constraints on the possible ways it can be solved. Or do various species invent different solutions to the navigation problem, depending on their sensory and motor abilities, the kind of food they seek, the characteristics of their predators, and so forth?

At first glance, it seems likely that various species differ considerably in how they navigate (for a general overview of navigation in a comparative perspective, see Wiener et al., 2011). For example, some species have magnetic compasses or sonar capabilities, while others do not; some species migrate long distances, while others live out their lives in ancestrally-defined territories. However, despite these obvious differences between species, there may also be deeper commonalities. One such cross-species commonality in spatial navigation has been proposed to be the use of geometric information in the surrounding environment to reorient. Occasionally, we lose track of our position in the world, and must re-establish where we are located in order to function. Several kinds of information could guide this process, called reorientation.

One parsing of the information sources for reorientation proposes two classes of cues (Gallistel, 1990). Geometric cues involve the relation between at least two points or two surfaces; in the lab, this has been operationalized mainly by investigating the use of relative lengths or corner angles of enclosed surfaces. Any other cue to orientation has been termed, by default, non-geometric, or sometimes featural, and operationalizations have included the study of colored walls, beacons, and odors. More recently, a third type of cue – the slope of the floor of an enclosed search space – has been examined, and slope appears to be a powerful reorientation cue as well.

Reorientation research has revealed some powerful cross-species commonalities. It has also engaged the question of human uniqueness because it has been claimed that human adults reorient differently from other species, or from young human children, in a fashion grounded in the distinctive combinatorial power of human language. In this chapter, we consider the phenomenon of reorientation in comparative perspective, both to evaluate specific claims regarding commonalities and differences in spatial navigation, and also to illustrate, more generally, how comparative cognition research and research in human cognitive development have deep mutual relevance. We begin with the debate over the geometric module, as this issue has initiated and fueled research in the field. Following an exposition of the modular approach, we first discuss claims that human language confers a unique mode of operation on human adults and older children, and then proceed to other aspects of the modularity debate, and evidence for a non-modular position, i.e., adaptive combination theory. We then transition to two sections that are aimed at broadening the focus of the debate. The first of these sections focuses on a discussion of slope as a potential reorientation cue, how it might be differentially used across species, and if slope could be considered a particular type of either geometric or feature information, or is instead an entirely new cue class. The second case for a wider perspective comes from the fact that the reorientation literature has so far focused on the behavior of groups of individuals, for example, pigeons or mice or children of various ages, considered collectively. There is a growing trend to look for individual differences within species or age groups that might be predictors of behavior. Many spatial abilities have been studied in relation to individual and sex differences in performance, and we close with a discussion of recently reported sex-related differences in reorientation.

The Original Proposal: A Geometric Module

Ken Cheng (1986) was the first researcher to observe a difference between the search behavior of oriented and disoriented rats. His rats were allowed to search for food as they wandered around in a rectangular enclosure. Each of the corners was marked with distinctive feature cues of various kinds, e.g., the number of lights, the odor (see Figure 1). Once a rat found the correct corner, it was allowed to start eating, but partway through its meal, it was removed, disoriented, and then placed in an identical enclosure. It would seem quite logical for the rat to return to the corner at which there had been food, but this only happened 50% of the time. In this situation, rats favored the corners that were geometrically correct, but did not use other cues to disambiguate the two corners. For example, even when the correct corner smelled of peppermint, rats would sometimes return to the peppermint-scented corner, but equally often go to the rotationally equivalent corner that smelled of licorice. This behavioral pattern is found only for working memory versions of the task where the correct corner changes from trial to trial. In reference memory versions of the task, where the correct location remains stable over the course of the experiment, then over time rats are able to learn to use the non-geometric properties of the space.

To explain this suboptimal behavior on the working memory task, Cheng proposed the idea of a geometric module for reorientation. He argued that when rats return to the enclosed space, the geometry of the enclosure is the overriding cue that is used to re-set their spatial position so that the two corners with identical geometric properties are indistinguishable.  Importantly, the geometric information was proposed to be modular, in the sense of being encapsulated and impenetrable. This description captured the fact that rats discarded the useful feature information, even though it could have been used for better performance.

Gallistel (1990) proposed that the apparently suboptimal behavior observed in the lab might be quite advantageous in the natural world. He argued that the features of the environment change, sometimes over the course of the day as the sunlight shifts or weather patterns change, and also over the seasons, as when the leaves change color and when snow falls. Because the geometric properties of the environment are less changeable than other cues, such as odors, Gallistel proposed that there might have been selective pressure for a geometric module to evolve that excluded the variable feature properties and depended only on the stable geometric properties of the environment.

FIGURE 1 HERE

The Geometric Module-Plus-Language Hypothesis

Cheng’s findings and Gallistel’s analysis suggested that the geometric module might characterize the behavior ofmany species, including humans. Indeed, children between the ages of 18 months to six years of age seemed to perform the same as Cheng’s rats (Hermer & Spelke, 1994, 1996). That is, they ignored a saliently-colored feature wall in a rectangular room, and instead searched for a hidden object in the two geometrically equivalent corners (see Figure 2). Since children and rats performed similarly, it appeared that the reorientation module was evolutionarily ancient and conserved across species. However, human adults, in contrast to rats and toddlers, were able to flexibly combine feature and geometric information and searched almost exclusively for the hidden object at the correct corner. The fundamental difference between the reorientation behavior of rats and children on the one hand and adults on the other hand was proposed to be due to the production of spatial language that enabled flexible adult performance.

FIGURE 2 HERE

Support for the geometric module-plus-language account came from two primary lines of research. First, it was found that, for children between the ages of 5 and 6 years, there was a correlation between production of the words “left” and “right” and successful performance on the reorientation task (Hermer-Vazquez, Moffett, & Munkholm, 2001). The second empirical approach was to try to eliminate adults’ use of language during the reorientation task (Hermer-Vazquez, Spelke, & Katsnelson, 1999). When adults were asked to perform a verbal shadowing task at the same time as the reorientation task, their reorientation behavior fell back to exclusive geometric choices similar to those of the rats and young children. These two lines of evidence were taken as support that children were limited to using geometric information for reorientation until they acquired spatial language production capabilities that enabled them to flexibly integrate feature and geometric cues.

Initial Comparative Work

Troubling evidence for the geometric module-plus-language position seemed to come from comparative data gathered since Cheng’s original work. Features turned out to actually be often used for reorientation across a wide range of non-human animals, including chickens (Vallortigara, Zanforlin, & Pasti, 1990), pigeons (Kelly, Spetch, & Heth, 1998), monkeys (Gouteux, Thinus-Blanc, & Vauclair, 2001; see Figure 3 below),fish (Sovrano, Bisazza, & Vallortigara, 2003), mice (Twyman, Newcombe, & Gould, 2009), and ants (Wystrach & Beugnon, 2009). It is obviously unlikely that feature use in these non-human species could be explained through language.

INSERT FIGURE 3

There are problems, however, with regarding these data as invalidating either the modularity hypothesis or the unique role of human language. First, many of the studies used a reference memory paradigm, in which correct search remains constant across trials. Cheng (1986) had only found modularity effects in working memory, where the correct location changes from trial to trial. Second, Hermer-Vazquez et al. (2001) objected that studies with non-human animals involve extensive training. They suggested that the distinctive power of human language comes from its ability to allow for flexible use of features without training.

Is Language Necessary for Feature Use in Reorientation?

Because work with non-human animals involves training regimens by necessity, the hypothesis that human language has a unique role can really only be examined in the human species. Focusing only on the human evidence, there is reason to doubt that language is necessary for the puncturing of a geometric module by feature cues. First, each of the two lines of supportive research presented earlier can be questioned. There are puzzling aspects to the Hermer-Vazquez et al. (2001) data, such as why it is the production of spatial terms that is associated with better performance, rather than comprehension. Additionally, as suggestive as the data are, it is possible that a third variable could account for the relationship between language production and flexible reorientation. There are also problems with the verbal shadowing experiments. While they seem to give stronger evidence than the correlational data, subsequent research has failed to replicate the dramatic fall to chance for adults concurrently performing the reorientation and verbal shadowing task. Furthermore, and crucially, while reorientation performance does diminish to some extent with verbal shadowing, the effect is not particular to a linguistic task but also occurs with spatial shadowing tasks (Hupbach, Hardt, Nadel, & Bohbot, 2007; Ratliff & Newcombe, 2008a). These data seem to suggest that, while language is a useful tool for adults, it is not a necessity.

Second, if language were crucial, it would seem that individuals with language problems should perform like young children on the reorientation task. There are two tests of this idea. In one experiment, individuals with global aphasia performed no differently from control participants (Bek, Blades, Siegal, & Varley, 2010), suggesting that the flexible behavior observed with human adults does not depend exclusively on the availability of language (although perhaps having been able to speak for many years could be argued to have crucially affected spatial reorientation). In the second experiment, deaf individuals in Nicaragua who had grown up in an environment without input from a structured sign language performed less well than deaf individuals in a second, later-born cohort who did have such input (Pyers, Shusterman, Senghas, Spelke, & Emmorey, 2010). However, the first cohort still searched at the correct corner far more than would be expected by chance (67.5% as opposed to 25% chance). Further, other aspects of the data set indicated that the first cohort had been deprived in ways that led to spatial deficits more global than deficits in feature use for reorientation. They also performed less well than the second cohort in a rotated box condition that did not involve reorientation, and they showed an odd pattern of errors in the reorientation study, in which rotational errors did not predominate, as is almost universal in reorientation studies.

Third, and most decisively, it has turned out that toddlers can in fact use features to reorient. Although far too young to be able to use or comprehend the terms left and right, and often with little spatial language at all, children as young as 18 months can succeed in using a colored wall to find the correct corner in a rectangular room, as long as the room is somewhat larger than the very small room used in the initial Hermer and Spelke studies (Learmonth, Newcombe, & Huttenlocher, 2001). We will review the room size effect in more detail below.

In sum, there is reason to doubt the position that language is the mechanism that facilitates a more flexible reorientation strategy in adults compared to children and non-human animals. However, this is not to say that language is not helpful. There is evidence that even just hearing relevant spatial language (at the red wall) or task relevant non-spatial language (red can help you) can be a powerful tool to help children succeed at reorientation tasks before they are normally able to reorient with a feature cue (Shusterman, Lee, & Spelke, 2011).

Are Features Really Used by Children to Reorient?

Lee, Shusterman, and Spelke (2006) and Lee and Spelke (2010) have proposed an alternative account for the apparent use of features by children and non-human animals. They argue that true reorientation can only be accomplished with geometric cues; in a separate process, features can be used to guide search to the target location, but features are not used to update position in the environment. To test this hypothesis, Lee et al. (2006) asked children to reorient in an enclosed circular space, which does not provide any useful geometric information. Three objects forming an equilateral triangle were placed in the middle of the enclosure. One of these objects was unique (a red cylinder) and two of the objects were identical (blue boxes). Lee et al. argued that the unique red cylinder could act both as a beacon (a feature that directly marks a hiding location) and also as a landmark (a feature that indirectly marks a hiding location) that could in theory differentiate search between the two identical blue box locations. For example, children might orient themselves to the red cylinder and then remember that the hiding location was the blue box on the left. This kind of performance was not found. Children searched almost perfectly at the unique container (a beacon) but divided search evenly (i.e., randomly) between the two blue containers. The authors reasoned that if features were truly capable of being used for reorientation, then children should succeed at the task when the target is hidden in any of the three containers. Therefore, it was argued that children remained disoriented in the absence of a geometric cue, but were nonetheless able to use a beacon to retrieve a hidden object.

As reorientation experiments are often conducted in rectangular enclosures, the two-step account could potentially explain the use of features by non-human animals and young children in the majority of studies to date. In the first step, the only true reorientation step, the participant or subject is able to reorient by the geometry of the space which narrows the possible search locations to two geometrically correct places. In the second step, the participant or subject chooses either the white-white geometrically correct or white-colored geometrically correct corner by beaconing to the correct target location. Thus, the Lee et al. (2006) experiment suggested that a two-step account for reorientation, with true reorientation based on geometry and beacon piloting accounting for feature use, might explain use of features by young children and non-human animals.

This study is not, however, decisive. Some of the parameters of the Lee et al. (2006) study may have made features less likely to be used for reorientation. First, although the area of the circular enclosure was quite large, the actual area of the array of objects was small. It has been demonstrated that features are less likely to be used in small spaces (Learmonth, Newcombe, & Huttenlocher, 2001; Learmonth, Nadel, & Newcombe, 2002). Features are more likely to be used for orientation when they are further away (called distal cues) because they are more accurate for indicating direction than when they are close to the hiding location (proximal cues) where left-right relations can change as one moves around the target location (Nadel & Hupbach, 2006). Second, the feature was itself a hiding container, and thus it is not surprising that it was used as a beacon. Third, the feature appeared small and portable, and in fact the children watched the experimenter move the hiding locations. Mobile parts of the environment are not reliable cues for determining a heading. Fourth, different brain regions appear to be activated when the feature is located inside a space, as opposed to against or on the periphery of an enclosure. From the animal literature, features along the periphery of the enclosure control hippocampal place cell firing, while the same landmark inside the enclosure does not (Cressant, Muller, & Poucet, 1997, 1999; Zugaro, Berthoz, & Wiener, 2001). All of these factors make it more likely that the unique container would be coded by children as a beacon, rather than as a landmark for reorientation.

In fact, there is some evidence that features can be used as a heading cue for reorientation. In square rooms, there are no useful geometric cues to aid reorientation. Success in this task would therefore depend on the use of feature cues. In square environments, toddlers are able to reorient using relative feature cues such as large versus small polka-dot patterns (Huttenlocher & Lourenco, 2007; see Figure 4) and distinct colors (Nardini, Atkinson, & Burgess, 2008). This effect was also found for mice (Twyman, Newcombe, & Gould, 2009). However, a possible rebuttal from modularity theorists would be that performance in this paradigm is based on the use of complex beacons. The corners of the enclosure can be distinguished from adjacent corners (although not from the diagonally opposite corners) based on the left-right positions of each feature (i.e. the corners might be blue/red, or red/blue). It is therefore possible that the combination of features, including relative position information, could be used as a beacon, leaving open the possibility that feature use in these experiments might be accounted for by an associative model.

FIGURE 4 HERE

More directly, Newcombe, Ratliff, Shallcross, and Twyman (2010) designed an experiment to directly test the Lee et al. (2006) claims. In the first experiment, children were asked to reorient in an octagon with alternating short and long walls. In this type of enclosure, the eight possible hiding locations can be reduced to the four geometrically equivalent corners that share the same wall length and sense relations to the target location (see Figure 5). For example, a participant could use the geometry of the octagon to remember that the correct location is in one of the corners with a long wall to the left and a short wall to the right. Different groups of children were asked to reorient in the octagonal space either with or without one of the walls of the octagon serving as a red feature wall. This cue could be used, for example, to remember that the target is on the left side of the red wall.

The first finding was that, in an all-white (geometry-only) condition, 2- and 3-year-old children were able to use the complex geometry of the octagon for orientation. The fact that toddlers were able to use the geometry of the octagon was quite remarkable given the complexity of the shape, the subtle obtuse corner angles, and the lack of a single principal axis of space that might have helped reorientation. The second finding was that, when a feature wall was added, 3-and 5-year-old children were able to choose among the three all-white corners that share the same geometric and feature properties; these corners can only be distinguished on the basis of indirect feature use of the red wall. (Two-year-old children were not tested.) The octagon experiments demonstrate that children are able to use the feature for true reorientation, at least in the presence of geometric information.

To determine what happens in the absence of geometric information, a second experiment was conducted in a circle with a design similar to that of the Lee et al. (2006) study. Four year old children were asked to reorient in a circular enclosure and were asked to find a hidden object in small hiding boxes (see Figure 6). The most important difference between the Lee et al. and Newcombe et al. experiments is that in the former, the feature is actually one of the hiding locations and is centrally placed within the enclosure while in the latter the feature is a stable part of the enclosure boundary. When the feature is stable and integrated into the space, children are able to reorient with the feature cue. They are able to correctly search at a hiding location within an array of either two or three boxes placed in the middle of the enclosure. Together, studies with children that use a more stable feature cue suggest that features are truly used for reorientation, and not just as beacons marking the target. There are at least two lines of research that could extend these findings. For the first, although children searched above chance in the octagon and circle experiments, adults were quite a bit more accurate. Therefore it appears that both the use of geometric and feature cues develops beyond the first five years of life. These paradigms could be used to chart the developmental trajectories of both cue classes. In a complementary fashion, it would be interesting to extend these paradigms with nonhuman animals to determine if they too are able to truly use feature cues for reorientation.

FIGURE 5 HERE

An Alternative Proposal: Adaptive Combination Theory

Spatial memory and judgments are typically based on a variety of cues, and there is evidence that these cues are combined in a Bayesian fashion (Cheng, Shettleworth, Huttenlocher & Rieser, 2007; Huttenlocher, Hedges & Duncan, 1991; Waismeyer & Jacobs, 2012). This idea can be applied to the data on use of geometric and featural cues. In contrast to modularity theory, adaptive combination theory proposes that geometric and featural cues can both be used for reorientation in a fashion that depends on a combination of cue weights, with the weights determined by factors such as the perceptual salience of the cues (which affects their initial encoding), the reliability of the memory traces (i.e., subjective uncertainty, which is related to the variability of estimates), and the success with of that kind of cue given prior experience (Newcombe & Huttenlocher, 2006; Newcombe & Ratliff, 2007). Information that is more salient, more reliable as a predictor of the goal, more familiar, or low in variability, should be taken into account more than other competing sources of information. The flexibility of adaptive combination theory suggests that, when features and geometry have similar weights on these dimensions, they should be integrated, but when the combination of weights on these dimensions strongly favors one kind of cue over the other, that cue should dominate.

We should pause for a moment to discuss cue salience.Geometry and features have been the main cue classes that have been examined in the reorientation literature. It might be argued that it is difficult to compare how much each contributes to behavior because the saliencies of the cue type are impossible to equate, and may well differ across periods of development or between species. While it is true that the absolute salience of each cue cannot be know for each participant or subject, what is important for adaptive combination theory is that the salience can be varied. For any given situation, when the salience of the cue is increased, then the adaptive combination theory predicts that it will be more heavily used. This kind of finding has been demonstrated. For example, we see a reduced reliance on geometric information in increasingly large rooms (the room size effect discussed below) where the feature cue becomes more salient because it is more distal. As another example, when subjects have spent the early part of their lives in either geometrically or featurally rich environments, we see rearing effects, also to be discussed further below.

FIGURE 6 HERE

Despite the strengths of the adaptive combination approach, its potential weakness is being overly general, and future work clearly needs to more rigorously specify the parameters in a well-defined model, and test novel predictions. Nonetheless, in this section we review the data that suggest that some model more flexible than geometric modularity is necessary.

The Room Size Effect

In an important illustration of adaptive combination theory, and a challenge to modularity theorists, the dominance of geometric information over feature use, has turned out to depend critically on the size of the enclosure. Geometry is more likely to be used in small spaces and features are more likely to be used in large spaces, for children (Learmonth et al., 2001, 2002, 2008), adults (Ratliff & Newcombe 2008b), fish (Sovrano et al., 2007), chicks (Chiandetti et al., 2007; Sovrano & Vallortigara, 2006;  Vallortigara et al., 2005), and pigeons (Kelly et al., 1998). These data cannot be explained by any interesting version of modularity theory because an adaptive module should operate across variations in scale and should especially operate in large spaces. It is true that there might be a module that applies only to very small enclosures, but it is hard to see how such a module would be central to survival and reproduction in any plausible environment of adaptation.

Why does the size of the space make a difference? One possibility is that the geometric cue is more salient in small spaces because the relative difference between wall lengths is more noticeable when the aspect ratio is greater and when the wall lengths can be compared within a single view. Therefore, as the room size increases, the weight assigned to the geometry cue is reduced. However, the attractiveness of this idea is decreased by a recent demonstration that the distance of the walls from the center of the room is the potent cue in this paradigm, rather than the lengths of the walls (Lee, Sovrano & Spelke, 2012). If distance is more important than length, then one could postulate that differences in two short distances are easier to compare than differences in two longer distances.

There are other explanations for the room size effect. As the room size increases, the weight assigned to the feature cue is increased because a landmark is more useful for determining heading when it is a distal rather than a proximal cue (Lew, 2011). In addition, the increased possibility for movement in the larger room may engage more spatial processing. In several experiments on these issues, Learmonth, Newcombe, Sheridan, and Jones (2008) found that both the distance of features from the participant and the possibilities for action in the larger space have an impact on the age at which children succeed in using features. The changing relative use of geometric and feature cues based on the scale of space is difficult for the modular position to explain, as it would predict invariant use of geometry. In contrast, the changing weights of cues as a function of their salience and reliability are at the heart of adaptive combination theory.

Short-Term Experience Effects

Experience effects are not predicted by modularity theory; modules are supposed to be inflexible and relatively impermeable. However, adaptive combination theory suggests that familiarity with a cue should be an important determinant of use of features versus geometry. There are several training experiments that provide support for the effects of recent experience. In one study, children were given practice using a feature for reorientation in an equilateral triangle (no useful geometry) with three different colored walls (Twyman, Friedman, & Spetch, 2007). In as few as four practice trials with the feature, 4- and 5-year-old children came to use the feature wall to reorient even in the small space used by Hermer and Spelke (1994;1996), in which same-aged children had been shown to rely exclusively on geometric cues. The short training period was effective in either the presence (a rectangle) or absence (equilateral triangle) of relevant geometric information. This experimen highlights that the relative use of geometric and feature cues can change. Along similar lines, four trials of experience in a larger enclosure lead to young children’s use of features in the small enclosure (Learmonth et al., 2008).

Newcombe and Ratliff (2008b) demonstrated a similar pattern for adults. Participants were asked to perform a reorientation task in either a small or a large room and to switch room sizes halfway through the experiment. People who had started in the large room (where features are salient) relied more heavily on the feature cue than people who had spent all trials in a small room. In contrast, individuals who had started in the small room (where geometry is salient) began to use feature information when moved to the larger room; in fact, they performed no differently from individuals who had remained in the large room for all trials. Therefore, it seems likely that the successful search using the feature in the large space increased the relative dependence on the feature cue, and this change in relative cue weights was reflected when participants were asked to perform the same task in the smaller space.

Short-term experience also matters for pigeons. Kelly and Spetch (2004) trained pigeons on the reorientation task. Some of the pigeons were initially trained with geometry and others were trained with features. Then the pigeons experienced training with both cues and were tested for their relative use. The pigeons with the geometry pre-training relied both on geometric and feature cues, while the pigeons with the feature pre-training relied mainly on just the feature cues.

These experiments with children, adults, and pigeons indicate a common theme: reorientation is a flexible system that is updated, based on prior experiences. Next we turn to experiences over a longer period of time and earlier in development.

Rearing Effects

The previous sections demonstrated that changes in the salience of the cues or in the participants’ short-term experiences influence reorientation behavior. A series of rearing experiments have demonstrated that there are differences that emerge over a longer period, at least for some species. Initially, the reorientation ability of wild-caught mountain chickadees (Poecile gambeli) was examined (Gray, Bloomfield, Ferrey, Spetch, & Sturdy, 2005). This group of researchers used wild-caught birds as they were likely to have experienced rich feature information in their natural habitat. This species typically lives in forested areas near streams and mountains. The environment just described contrasts greatly with the standard housing conditions in labs, which are comprised largely of uniform rectangular enclosures. The wild-caught chickadees relied more heavily on feature cues than did other standard-reared species. However, when the reorientation abilities of wild-caught and lab-reared black-capped chickadees (Poecile atricapillus) were examined, their behavior was much closer to the standard-reared subjects (Batty, Bloomfield, Spetch, & Sturdy, 2009). Therefore, it is unclear if there is something different about the experiences of black-capped and mountain chickadees that cause these differences, or if there is a difference across species.

An alternative approach is to tightly control the rearing environment. This approach has been used with chicks, fish, and mice. For chicks, there does not seem to be any difference between chicks reared in a circular (lacking relevant geometry) and rectangular (containing relative wall lengths) environment in their relative use of feature or geometric cues (Chiandetti & Vallortigara, 2008, 2010). However, the chicks were only housed for two days before starting training, and they are a precocial species that may not have as much of a sensitive period for rearing effects. In experiments with longer rearing periods, a different pattern has emerged. Convict fish were reared in either circular or rectangular environments. Subsequent tests showed that the fish in the circular environments relied more heavily on feature cues than did the rectangular reared fish (Brown, Spetch, & Hurd, 2007).

Similar to fish, there are differences between mice that have been raised in feature rich environment (a circle with one half white one half blue) and a geometrically rich environment (rectangular enclosur with a triangular nest box; see Figure 7). Although there were no differences in the acquisition of geometric information alone, the circular-reared mice were faster to learn a feature panel task. Additionally, and crucially, on a test of incidental geometry encoding (a rectangle with a feature panel marking the correct location), the rectangular- reared mice had encoded the geometry while the circular-reared mice had not (Twyman, Newcombe, & Gould, 2012).

FIGURE 7 HERE

In summary, for chicks and black-capped chickadees, early environment does not have a large impact on reorientation behavior. However, for mountain chickadees, mice, and fish, the rearing environment alters the relative use of geometric and feature cues.

Facilitation and Interference Effects

One reason given initially to favor a modularity hypothesis was the claim that geometric and featural information are both learned in situations where one might expect overshadowing or blocking effects (Cheng & Newcombe, 2005). This pattern of independence suggested separable systems. However, subsequent research has shown a far more complex pattern of results, with the two kinds of information sometimes learned independently, sometimes showing overshadowing or blocking of one by the other, and sometimes showing facilitation of one by the other (Cheng, 2008; Miller & Shettleworth, 2008). Furthermore, it has been shown that rats can integrate these kinds of information across successive phases of an experiment to make correct spatial choices (Rhodes, Creighton, Killcross, Good & Honey, 2009).

Just considering facilitation effects, there are two recent examples, one from research with birds and other from research with humans. Kelly (2010) trained two groups of Clark’s nutcrackers (Nucifraga columbiana) with an array of objects at the four corners of a rectangle. When the objects were identical, the birds did not learn the task after an extensive training program. When the objects were unique, the birds learned the task and, maybe surprisingly, had also encoded the rectangular shape of the array. In another example of a facilitation effect, human individuals with Williams Syndrome, a genetic defect that has important effects on spatial functioning, failed to encode the geometry of an all-white rectangular enclosure, but showed geometric encoding when a colored feature wall was added (Lakusta, Dessalegn & Landau, 2010).

This literature is now much too large to review thoroughly here, but it clearly challenges modularity theory (Twyman & Newcombe, 2010). More important, it represents a challenge to any viable comprehensive theory, which must be able to account in precise quantitative terms for the pattern of effects, and make novel predictions. An interesting direction for future research has been indicated by recent studies on rodents which suggest that cue interaction (blocking, overshadowing, and facilitation) between geometry and features might be modulated by sex because male and female rats tend to assign different weights to these cues (Rodriguez, Chamizo, & Mackintosh, 2011; Rodriguez, Torres, Mackintosh, & Chamizo, 2010); this should be explored in additional species.

Section Summary

The available evidence indicates that geometric and featural information can both be used for reorientation by a wide variety of species and (within the human species) across a broad range of ages. However, the relative use of these cues depends on their salience, the reliability of their encoding, and their familiarity across both recent and longer-term experience. Human language is one of several factors that can facilitate the use of features in situations in which it might otherwise be weak, but it is not the only way this end can be accomplished. From the general point of view of a field of comparative cognition, a striking fact is how vigorous the dialogue between the developmental and comparative communities has been, and how many species have been investigated using how many techniques. Wider development of this dialogue is likely to be very fruitful.

Slope as a Reorientation Cue

Most spatial experiments, including reorientation studies, have been conducted on flat surfaces. But, as we all know after climbing a hill or admiring an amazing view from a mountain top, the world is not flat. The slope of the terrain might clearly be an important cue for polarizing space, and hence for reorienting. One could imagine using “uphill” in a similar manner to “north” to anchor a direction in the environment. But is it in fact used this way?

Nardi and Bingman (2009a) compared the reorientation performance of pigeons which were trained to a correct corner of a trapezoid on a flat surface (geometry-only) with pigeons which were trained in the same trapezoid enclosure, but now with the floor sloped at 20 degree angle (geometry + slope, see Figure 8). Both groups of pigeons learned the task, but the geometry + slope group learned about three times faster than the geometry only group. The follow up tests for the geometry + slope group revealed that the pigeons had readily encoded slope (92% correct), had encoded geometry at above chance levels although accuracy was not very high (63%), and that the pigeons overwhelmingly preferred the slope-correct (75%) over the geometry-correct corner (0%) on conflict trials. Overall, these data suggest that slope is a powerful cue for reorientation compared to the geometry of the sides of the enclosure.

FIGURE 8 HERE

As acquisition was so much faster in the combined group, Nardi and Bingman wondered if slope might facilitate geometry acquisition. In a second experiment, they trained groups of pigeons with only geometry or with combined geometry and slope cues. Over the course of training, no differences were found in geometry acquisition between groups. Thus, it appears that geometry and slope cues neither facilitate nor inhibit learning of each other, a pattern traditionally interpreted as supporting the idea that they are fundamentally different classes of cues.

Thus far, geometry has been considered a single cue. As Sutton (2009) points out, there are several possible cue types of a geometric nature. These levels of geometric cues may be nested within each other, where local cues are located near the correct location and the global cues encompass relations in the larger space. For example, the trapezoid enclosures that have been reviewed thus far include two types of geometric cues: local corner angles (acute or obtuse) and global relations between relative wall lengths (for example a long wall to the right and a shorter wall to the left). Nardi, Nitsch and Bingman (2010) conducted a series of geometry and slope learning experiments with pigeons that examined the contributions of local and global geometry as well as slope to reorientation performance. Over the course of training, pigeons first learned to go to the two acute corners within the first three days. It took about nine days for pigeons to learn the global geometry of the space. Therefore, local geometry learning is much faster than global geometry learning. As one of the follow up tests, Nardi et al. rotated the training apparatus so that pigeons could not match all of the local geometry, global geometry, and the slope. In this manipulation, pigeons matched the correct slope and local geometric cue, at the expense of the global geometric cue. In training conditions where the global geometry is made two- or three-times as predictive as slope as an indicator of the correct target location, pigeons still rely more heavily on the slope rather than the global geometric cue. Therefore, for pigeons, the multimodal slope cue, which includes visual, kinesthetic, and vestibular information, appears to be particularly salient, and more important than geometry for a reorientation task.

Humans

Pigeons encode slope, but what about other species? The fact that pigeons can fly might be taken to argue that they are less likely to encode slope than species that cannot transcend the terrestrial environment, but is that in fact true? Nardi, Shipley and Newcombe (2011) put adult humans in a uniform white square enclosure with no useful geometric or feature cues for orientation. The 5° sloped floor of the enclosure provided visual, kinesthetic, and vestibular cues that could guide search (see Figure 9). A bowl was located in each corner of the room and participants saw a $1 bill hidden under one of the bowls. The correct hiding bowl remained the same for each of the four training trials for each participant, but was counterbalanced across subjects. After seeing the correct location, participants were disoriented and then asked to find the $1 bill. Once training was complete, two post-training tests compared search with the 5° sloped floor to the same space with a flat floor. People performed at chance (25%) when the floor was flat, showing that they had been thoroughly disoriented and that there were no stray cues that could be used to reorient. When the floor was sloped at a 5° angle, people were able to retrieve the hidden object on the majority of the trials, although there was a significant difference between men (79%) and women (43%) during the training trials. (This sex difference will be discussed further later in the paper.) This study showed that people can use slope as a reorientation cue, although less clearly than the pigeons had; however, the fact that the slope was at a much reduced angle for humans may have contributed to this apparent species difference. The Institutional Review Board declined Nardi et al. (2011) to tilt the floor of the room at a steeper angle. Therefore, studying pigeons (or other animals) at gentler angles would allow for a better comparison across species.

FIGURE 9 HERE

What Kind of Cue is Slope?

Thus far, we have seen that both an aerial species (pigeons) and a terrestrial species (people) use slope for reorientation; additionally, for pigeons, slope is a very powerful cue, which does not appear to interact with geometric cues in spatial learning. Now we turn to the question, important in the context of the reorientation literature, of whether to categorize slope as geometric information, feature information, or something else. There are arguments for slope being considered a geometric cue. The slope of the floor, say a 10 degree incline, is measured as the difference between a perfectly horizontal surface, perpendicular to gravitational force, and the angle of the floor. Therefore, the slope could be defined by comparing a surface to a surface in terms of angle, which would fall under Gallistel’s (1990) definition of a geometric cue. Additionally, determining that the floor is sloped could be accomplished by comparing relative lengths of walls (assuming a horizontal ceiling, the participant could judge the distance between the floor and the ceiling and note that the “up” end of the slope has a shorter wall height than the “down” end of the slope) or by noting the angle at which the floor meets the walls (acute at the uphill end and obtuse at the downhill end).

However, slope could be considered a type of feature information if viewed as a property of non-horizontal surfaces. One could use the slope direction to determine the facing orientation and to encode a location. For example, a navigator moving on a slope might know that the top of the hill should be on the left in order to get to a desired destination. This is analogous to the role that distant landmarks – another type of feature cue – play in horizontal environments; if there were a conspicuous landmark in the horizon (e.g., a mountain), then one could use it to determine heading. Therefore, slope polarizes the environment and provides a directional frame of reference that can be used for (re)orientation, in the same way as a distant landmark. In this sense, varying the inclination
of the tilt affects the salience of slope information (steeper slopes are obviously more salient than gentle ones), just like varying the size of a landmark makes it more or less salient.

Research from a neuroscience perspective with pigeons is relevant to this issue. Previously, it had been shown that bilateral lesions to the pigeon hippocampal formation, an analogous structure to the human hippocampus, disrupt the processing of geometric cues, but not feature cues (Vargas, Petruso, & Bingman, 2004). Similarly, Nardi and Bingman (2007) found that lesions to the left hippocampal formation of pigeons decreased reliance on geometry for reorientation. Pigeons that had undergone a control surgery performed identically to pigeons with a lesioned right hippocampal formation. Since the hippocampus appears to be more heavily involved in the use of geometric cues than feature cues in pigeons, Nardi and Bingman reasoned that lesions to the hippocampal formation should disrupt slope-based reorientation if slope is a type of geometric cue.

Nardi and Bingman (2009b) examined the reorientation of control and bilaterally lesioned pigeons when geometric and slope cues were available for reorientation. The training apparatus was a trapezoid shaped room with the correct
corner in one of the acute corners. Additionally, the floor was sloped at a 20 degree angle. Both groups of pigeons learned the task. Supporting previous research, the pigeons with the bilaterally lesioned hippocampal formation had more difficultly using the geometric cue than the control pigeons. Interestingly, there were no differences between groups in the use of slope. All pigeons rapidly learned the task, had encoded the slope cue when it was tested in isolation, and selected the slope correct corner on conflict trials. Therefore, it not only appears (again) that slope is a powerful reorientation cue for pigeons, since all pigeons preferred to reorient with slope rather than geometry, but also that slope does not seem to recruit the same neural circuits used by geometric cues. The identical performance of control and hippocampal lesioned pigeons with a slope reorientation cue implies that slope is hippocampal independent, and therefore is more like a feature cue than a geometric cue. The authors characterize slope as a gravity-dependent feature cue. However, given the distinctive characteristics of this cue – because it provides multimodal sensory stimuli, because it is associated with effortful movement, and because it involves the vertical dimension – it may be that slope is a unique type of information.

Slope Cues Versus Feature Cues In Pigeons and People

If slope cues are similar in some ways to feature cues, how do they interact and which kind of cue is more powerful?
Nardi and colleagues have asked these questions in behavioral studies with both pigeons and people. In both experiments, the experimental space was a square so that the geometric information was identical throughout the space. (Recall, however, that the floor was sloped at a 20 degree angle for pigeons and at a 5 degree angle for people.) Unique feature cards were placed in each corner of the room; therefore the correct target location could be identified based on the beacon alone.

Pigeons readily learned the reorientation task (Nardi, Mauch, Klimas, & Bingman, 2012). Post training tests indicated that the pigeons had encoded both cues. When slope (all feature cards identical) or beacon (flat floor) cues were presented in isolation, pigeons were highly accurate (96%). On the conflict test, where the trained beacon location was moved to an incorrect slope location, pigeons divided their search evenly between the beacon-correct and slope-correct corners. Interestingly, choices on the conflict tests depended on the location of the correct corner during training. When pigeons were required to go uphill during training, pigeons selected the slope-correct corner 76% of the time. In contrast, when pigeons went downhill to the correct training location, pigeons selected the beacon-correct corner 75% of the time. When pigeons go uphill, they exert more effort than when they follow the slope downhill. Nardi et al. propose that the role of effort might modulate the weighting of the slope and beacon cue for reorientation.

Using a similar paradigm, Nardi, Newcombe, and Shipley (2012) examined the interaction between slope and feature
cues with people. Like pigeons, people readily learned to reorient. Unlike pigeons, who encoded both the feature and
the slope, about two-thirds of the participants only encoded one or the other cue. Individuals performed similarly during the training trials with either the slope-strategy (78% accurate) or a feature-strategy (90% accurate). When people did not clearly follow a single strategy, they were not nearly as accurate, although still above 25% chance, on the training trials (50% accurate).

In sum, pigeons encode both slope and beacon cues during a reorientation task, with both information sources being
given equal importance. Interestingly, this balance seems to shift based on the amount of effort required during training. When pigeons require extra effort to go to an uphill location, then slope is given more importance than a beacon cue and vice versa. People are also able to encode and use slope and feature cues for reorientation. In contrast to pigeons, people tend to use a single strategy for reorientation, either a slopebased or a feature-based approach. They show consistent individual differences in which class of cue they prefer.

Section Summary

Overall, both pigeons and people are able to use slope as a reorientation cue. It appears that slope should be considered a different cue class from geometry. When the hippocampus of pigeons is lesioned, geometry performance is
impaired, particularly when the left hippocampal formation is lesioned. Slope behavior is unaffected by bilateral hippocampal formation lesions. Thus, slope and geometry appear to be processed by different areas of the pigeon brain. When pigeons are required to choose between feature, slope and geometry cue types, subjects weigh slope and feature cues about equally, and prefer to use slope over geometry. The over-reliance on slope when given also geometric information is compelling, as it occurs even if geometry is a better predictor of the goal. The balance between slope and feature cue use depends in part on the amount of effort during training. When the trained corner is located uphill, then pigeons rely more heavily on the slope cue. When the trained corner in located downhill, then pigeons rely more heavily on the feature cue. Thus, effort modulates the relative weighting of feature and slope cues in spatial memory for pigeons. When both slope and features are present during training, pigeons encode both cue types. In contrast, the majority of people tend to use one or the other cue type, in about equal proportions, to solve the reorientation task.

Sex Differences in Reorientation?

There are striking sex-related differences in some (but not all) kinds of human spatial functioning, particularly in mental rotation and in orientation to gravitationally-defined horizontal and vertical (Voyer, Voyer, & Bryden, 1995). There are also probably sex differences in navigation tasks. For example, men perform better than women in constructing a survey representation (Ishikawa & Montello, 2006), in using the geometry of a surrounding trapezoid to locate a hidden platform (Sandstrom, Kaufman, & Huettel, 1998), and in selecting the initial heading in a virtual Morris Water Maze task (Woolley, Vermaercke, Op de Beeck, Wagemans, Gantois, D’Hooge, Swinner, & Wenderoth, 2010). There are also probably sex differences in non-human species, although the differences vary across species, for example, mice show fewer such differences than rats (Jonasson, 2005).

Until recently, however, possible sex differences in reorientation have received little attention. In the animal literature, subjects are often all male, of unspecified sex, or comprise too small a sample to look for sex differences (Cheng & Newcombe, 2005). Of course, human studies of reorientation are more often able to look for sex differences, but they have mostly not found them. And when sex has been examined in studies of non-human animals, it seems to have weak and inconsistent effects (Sovrano, Bisazza, & Vallortigara, 2003 for fish; Twyman, Newcombe, & Gould, 2009, 2012 for mice). In sum, because of all-male samples, unknown sex, or too small sample sizes, it is unclear if there are differences between the sexes in reorientation, but they have not seemed impressive. However, more recently, some sex differences have emerged, concerning three areas. Arranged in ascending order by the power of the findings, they are: the use of local geometric cues, geometry in the presence of a beacon feature cue, and the use of slope for reorientation.

Local versus Global Geometry

It has been proposed that men rely more on directional cues such as cardinal position, gradients or distal landmarks, while women seem to depend on positional cues like local landmarks (Jacobs & Schenk, 2003). In a reorientation study linked to this issue, adults were asked to reorient in a space with both local and global reorientation cues (Reichert & Kelly, 2011). An array of four posts formed a mental rectangular search space that could be used as a global cue (see Figure 10). The diagonal pairs of corner posts were set at angles of either 50 or 75 degrees and served as local geometric cues.

FIGURE 10 HERE

Neither sex encoded the global geometric shape of the array; men, but not women, encoded the local geometric
cues (i.e., angle size). Therefore, men appeared to be better able to use local geometric cues for reorientation than were women, in contradiction of the Jacob and Schenk hypothesis. These findings are puzzling, however, not only because they seem to contradict the Jacobs and Schenk hypothesis, but also because Sutton, Twyman, Joanisse and Newcombe (2012) found that, at least in virtual reality, adults could infer the global geometric shape from and array of four columns. Additionally, Lubyk, Dupuis, Gutiérrez, and Spetch (2012) found that adults were able to reorient with local acute and obtuse angles in a virtual reality search task, and importantly, no sex differences were found.

Beacon Cues and Geometric Cues

The bulk of the previous research with humans has used a rectangular enclosed space as the geometric cue, and one of the walls of the rectangle was a unique color to provide the feature cue. In this type of task, gender differences have not been found with adults or with children (Hermer & Spelke 1994; 1996; Learmonth, Nadel, & Newcombe, 2002; Twyman, Friedman, & Spetch, 2007). However, two studies have used a distinctive object directly at or near the correct hiding location within a rectangular search space, i.e., a beacon. Kelly and Bischof (2005) created a 3D virtual environment of a rectangular search space. In each corner of the room was a distinctive object. Both men and women readily learned the task, which could have been accomplished by either encoding both the geometric cue and the beacon, or just paying attention to the beacon. When the beacons were removed, it was found that the men, but not the women, had encoded the geometry of the space. Importantly, in a similar experiment, when a feature wall was used, the sex difference went away and both men and women encoded the geometry of the space (Kelly & Bischof, 2008). Lourenco, Addy, Huttenlocher and Fabian (2011) found similar results with toddlers. In a real-world version of the task with an enclosed rectangular search space and either a unique hiding container or a distinctive flag placed on top of the target container, toddlers learned to reorient. On the geometry-only test in which all of the containers were identical, only the boys turned out to have encoded the geometry of the enclosure.

On the basis of these two studies, it is possible that gender differences in reorientation are specific to the case in which there are salient beacons, which somehow have an especially strong pull on females. It would be nice to know
the pattern with non-human animals, but researchers will need to use female as well as male animals to answer this
question. However, some geometric information may exist even when geometry-only tests are failed. Lourenco et al.
(2011) included conflict trials designed to assess the relative use of geometric and feature cues. All toddlers preferred the beacon cue to geometry, and all toddlers, both boys and girls, were slower to respond on the conflict trials than they had been during training. If the girls truly had not encoded the geometry during training, then their search times should have remained fast. Thus, the girls probably had noticed something about the shape of the environment even though not at a level sufficient to support active search with the geometric cue.

Sex Differences in Slope Cues

As we reviewed earlier, people are able to reorient with slope as the sole orientation cue (Nardi et al., 2011). Participants were disoriented in a uniform square room and then were asked to find a target location using the floor that was slanted at a 5° angle. Overall, people were able to use the sloped floor to guide search. However, men and women performed quite differently on this task. When participants were not given any extra instructions, men were about 35% more accurate (1.4 standard deviation difference). Additionally, each sex adopted different strategies. The vast majority of the men reported using slope, while only about half of women attempted to use the slope. The other half attempted to use other ineffective strategies: about a third of the women attempted to use a path integration strategy (trying to keep track of the number of rotations), and the remaining tried to use small features in the environment like a wrinkle in the fabric or a filament thread in the light bulb. Therefore, it is possible that the lower accuracy of women on this task could be because of strategy choice rather than a difference in ability.

In an effort to make the sloped floor more salient, Nardi et al. showed a ball rolling down the floor and told participants that the slanted floor could help them succeed at the task. All participants reported using a slope-based strategy. And people did improve, but men were still more accurate than women. To further investigate this sex difference, the authors wondered if women might have a difficult time perceiving the slanted floor. To test this hypothesis, participants were required to stand in the middle of the room and they were asked to point in the up direction of the slope as quickly and accurately as possible. Both sexes were able to correctly identify the direction of the slope in just over 3 seconds, but men were over 1 second faster than women.

Might women have more difficulty using slope since they are often wearing heeled footwear that might make slope
difficult to perceive and use? Probably not. In Nardi et al. (2011), when the footwear was uncontrolled (i.e. women
performed the task in the shoes they showed up with on the given day) and when women were required to wear flat slippers provided by the experimenters, they performed identically in the slope task. Further, when Nardi, Newcombe, and Shipley (2012) asked women to complete a survey about the height of footwear they wear for everyday use, there was no correlation between slope use and typical heel height. On a different note, an interesting aspect of this study was the finding that men were generally more confident in solving the reorientation task on a slope, suggesting that sex differences in spatial confidence might play a role in the performance advantage with slope.

In summary, there appear to be large sex difference in the use of slope-based strategies for reorientation. Men are more accurate than women by about 1.4 standard deviations, a difference that is larger than the sex difference for the mental rotation test. Men are twice as likely to adopt a slope based strategy when this is the only effective cue available. If the slope is made more salient, then almost everyone attempts to use slope, but men are still more accurate. Women are also slower to correctly identify the direction of a slope.

Section Summary

In the vast majority of studies with animals and humans, sex differences have not been found for reorientation behavior, particularly when experiments are conducted in enclosed rectangles with a feature wall. More recently, there have been a few findings that suggest sex differences when reorientation is tested with embedded local and geometric cues or with a beacon as a feature cue. From this small set of findings, it appears that men might be better at using geometric cues compared to women. This would parallel the sex differences that have been found for rats in water-maze search tasks, where both sexes can use geometric or proximal feature cues to locate a hidden platform, but male rats rely more heavily on geometric cues and female rats prefer to use a proximal feature cue (Rodriguez, Chamizo, & Mackintosh, 2011; Rodriguez, Torres, Mackintosh & Chamizo, 2010). However, these studies were not about orientation, and therefore any claims about sex differences in reorientation ability are currently far from definitive. Further experiments with nonhuman animals would be more likely to shed light on sex
differences than work with humans because various social and cultural differences could be excluded. Nevertheless, the most striking sex difference we have reviewed concerns the use of slope cues. Comparative work and work investigating the neural bases of these effects might shed more light on these differences.

Conclusion

Research on spatial cognition has been generally more open to a comparative approach than research in many other domains, and research on the geometric module theory has been an especially vigorous example of the kind of interchange that would be desirable for a comprehensive account of cognitive biology. In this article, we have seen that each field and sub-field often contributes distinctive methods and concepts to the collective enterprise. As a result, we know a great deal more about reorientation than we did in 1986. It has become clear that the twin hypotheses of a geometric module and a unique and necessary role for human language in reorientation cannot stand. It has also become clear that there is a need for expansion of the taxonomy of cues that can be used for reorientation, with slope a good example. There is also a need for definitional clarification and possibly for a change in nomenclature, because it is difficult to postulate a geometry that includes distance and direction, but not angle and length as suggested by Lee et al. (2012). It may be that a renewed focus on contact with the overall literature on spatial navigation will lead to a more comprehensive view (Lew, 2011). The challenge for the future will be in formulating a precise, quantitatively-specified model that can account for the hundreds of effects found to date, with more data being reported each month.

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Contact Information

Alexandra Twyman,
University of Western Ontario,
London, Ontario, Canada, N6A 3K7
Email: atwyman3@uwo.ca

Animals Prefer Reinforcement that Follows Greater Effort: Justification of Effort or Within-Trial Contrast?

Thomas R. Zentall
University of Kentucky

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Abstract:

Justification of effort by humans is a form of reducing cognitive dissonance by enhancing the value of rewards when they are more difficult to obtain. Presumably, assigning greater value to rewards provides justification for the greater effort needed to obtain them. We have found such effects in adult humans and children with a highly controlled laboratory task. More importantly, under various conditions we have found similar effects in pigeons, animals not typically thought to need to justify their behavior to themselves or others. To account for these results, we have proposed a mechanism based on within-trial contrast between the end of the effort and the reinforcement (or the signal for reinforcement) that follows. This model predicts that any relatively aversive event can serve to enhance the value of the reward that follows it, simply through the contrast between those two events. In support of this general model, we have found this effect in pigeons when the prior event consists of: (a) more rather than less effort (pecking), (b) a long rather than a short delay, and (c) the absence of food rather than food. We also show that within-trial contrast can occur in the absence of relative delay reduction theory. Contrast of this kind may also play a role in other social psychological phenomena that have been interpreted in terms of cognitive dissonance.

Keywords: cognitive dissonance, justification of effort, contrast, delay reduction

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When humans behave in a way that is inconsistent with the way they think they should behave, they will often try to justify their behavior by altering their beliefs. The theory on which this behavior is based is known as cognitive dissonance theory (Festinger, 1957). Evidence for the attempt to reduce cognitive dissonance comes from the classic study by Festinger and Carlsmith (1959) who found that subjects, who were given a small reward for agreeing to tell a prospective subject that a boring task was interesting, then rated the task more interesting than subjects who were given a large reward. Presumably, those given a small reward could not justify their behavior for the small reward so, to justify their behavior, they remembered the task as being more interesting. On the other hand, those given the large reward did not have to justify their behavior because the large reward was sufficient.

But the theory that such decisions are cognitively influenced has been challenged by evidence that humans with retrograde amnesia show cognitive-dissonance-like effects without having any memory for the presumed dissonant event (Lieberman, Ochsner, Gilbert, & Schacter, 2001). Lieberman et al. asked amnesics, to choose between pictures that they had originally judged to be similarly preferred. When they then asked the subjects to rate the pictures again, they, much like control subjects, now rated the chosen pictures higher than the unchosen pictures. What is surprising is that the amnesics had no memory for ever having seen the chosen pictures before. This result implies that cognitive dissonance is an implicit automatic process that requires little cognitive processing.

The same conclusion was reached by Egan, Santos, and Bloom (2007) who examined a similar effect in 4-year-old children and monkeys. When subjects were required to choose between two equally-preferred alternatives, they later avoided the unchosen alternative over a novel alternative, but they did so only if they were forced to make the original choice.

Festinger himself believed that his theory also applied to the behavior of nonhuman animals (Lawrence & Festinger, 1962), but the examples that they provided were only remotely related to the cognitive dissonance research that had been conducted with humans and the results that were obtained were easily accounted for by simpler behavioral mechanisms (e.g., the partial reinforcement extinction effect, which was attributed by others to a generalization decrement [Capaldi, 1967] or to an acquired response in the presence of frustration [Amsel, 1958]). Thus, the purpose of the research described in this article is to examine an analog design that could be used with nonhuman animals to determine if they too would show a similar cognitive dissonance effect.

One form of cognitive dissonance reduction is the justification of effort effect (Aronson & Mills, 1959). When a goal is difficult to obtain, Aronson and Mills found that it is often judged to be of more value than the same goal when it is easy to obtain. Specifically, Aronson and Mills reported that a group that required a difficult initiation to join was perceived as more attractive than a group that was easy to join. This effect appears to be inconsistent with the Law of Effect or the Law of Least Effort (Thorndike, 1932) because goals with less effort to obtain should have more value than goals that require more effort. To account for these results, Aronson and Mills proposed that the difficulty of the initiation could only be justified by increasing the perceived value of joining the group.

Alternatively, it could be argued that there may be a correlation between the difficulty in joining a group and the value of group membership. That is, although there is not always sufficient information on which to determine the value of a group, a reasonable heuristic may be that the difficulty of being admitted to the group is a functional (but perhaps imperfect) source of information about the value of group membership. Put more simply, more valuable groups are often harder to join.

The problem with studying justification of effort in humans is that humans often have had experience with functional heuristics or rules of thumb and what may appear to be a justification of effort, may actually reflect no more than the generalized use of this heuristic. On the other hand, if cognitive dissonance actually involves implicit automatic processes, cognitive processes may not be involved and one should be able to demonstrate justification of effort effects in nonhuman animals, under conditions that control for prior experience with the ability of effort to predict reward value.

The beauty of the Aronson and Mills (1959) design is that it easily can be adapted for use with animals because one can train an animal that a large effort is required to obtain one reinforcer whereas a small effort is required to obtain a different reinforcer. If the two reinforcers are objectively of equal value, one can then ask if the value of the reinforcer that requires greater effort is then preferred over the reinforcer that requires lesser value. Finding two reinforcers that have the same initial value and, more important, reinforcers that will not change in value with experience (unrelated to the effort involved in obtaining them) is quite a challenge (but see Johnson & Gallagher, 2011). Alternatively, one could use a salient discriminative stimulus that signals the presentation of the reinforcer following effort of one magnitude and a different discriminative stimulus that signals the same reinforcer following effort of another magnitude. One can then ask if the animal has a preference for either conditioned reinforcer, each having served equally often as a signal for the common reinforcer.

In this review, I will first present the results of an experiment in which we have found evidence for justification of effort in pigeons and then will describe a noncognitive model based on contrast to account for this effect. I will then demonstrate the generality of the effect to show that a variety of relatively aversive events can be used to produce a preference for the outcome that follows. We have interpreted the results of these experiments in terms of within-trial contrast and have proposed that it is unlike the various contrast effects that have been described in the literature (incentive contrast, anticipatory contrast, and behavioral contrast). Although an alternative theory, delay reduction, can make predictions similar to within-trial contrast, in several experiments we have found that within-trial contrast can be found in the absence of differential delay reduction. Although several studies have reported a failure to find evidence for within- trial contrast, the procedures and results of these studies have proven useful in identifying some of the boundary conditions that appear to constrain the appearance of this effect. Finally, I will suggest that contrast effects of this kind may be involved in several psychological phenomena that have been studies in humans (e.g., general cognitive dissonance effects, the distinction between intrinsic and extrinsic reinforcement, and learned industriousness).

Justification of Effort in Animals

To determine the effect of prior effort on the preference for the conditioned reinforcer that followed, Clement, Feltus, Kaiser, and Zentall (2000) trained pigeons with a procedure analogous to that used by Aronson and Mills (1959). All training trials began with the presentation of a white stimulus on the center response key. On half of the training trials, a single peck to the white key turned it off and turned on two different colored side keys, for example red and yellow, and choice of the red stimulus (S+) was reinforced but not the yellow stimulus (S-) (sides were counterbalanced over trials and colors were counterbalanced over subjects). On the remaining training trials, 20 pecks to the white key turned it off and turned on two different colored side keys, for example green and blue, and choice of the green stimulus was reinforced (see design of this experiment in Figure 1). Following extensive training, a small number of probe trials was introduced (among the training trials) involving the two conditioned reinforcers (i.e., red and green) as well as the two conditioned inhibitors (i.e., yellow and blue) to determine if the training had resulted in a preference for one over the other.

Interestingly, traditional learning theory (Hull, 1943; Thorndike 1932) would predict that this sort of training should not result in a differential preference because each of the conditioned stimuli would have been associated with the same reinforcer, obtained following the same delay from the onset of the conditioned reinforcer, and following the same effort in the presence of the conditioned reinforcer. That is, the antecedent events on training trials (the number of previous pecks experienced prior to the conditioned reinforcer during training) should not affect stimulus preference on probe trials.

Alternatively, one could imagine that stimuli that had been presented in the context of the single peck requirement would be associated with the easier trials and stimuli that had been presented in the context of the 20-peck requirement would be associated with the harder trials. If that was the case, it might be that the conditioned stimulus that was presented on the single-peck trials would be preferred over the conditioned stimulus that was presented on the 20-peck trials.

If, however, cognitive dissonance theory is correct, it could be that in order to “justify” the 20-peck requirement (because on other trials only a single peck was required) the pigeons would give added value to the reinforcer that followed the 20-peck requirement. If this was the case, the added value might transfer to the conditioned reinforcer that signaled its occurrence and one might find a preference for the stimulus that followed the greater effort.

Finally, it is possible that the peck requirement could serve as an occasion setter (or conditional stimulus) that the pigeons could use to anticipate which color would be presented. For example, if a pigeon was in the process of pecking 20 times it might anticipate the appearance of the green conditioned stimulus that it would choose. That is, the pecking requirement could bias the pigeon to choose the color that was most associated with that requirement. On probe trials there would be no peck requirement but Clement et al. (2000) reasoned that on probe trials, without an initial peck requirement, the pigeons might be biased to choose the conditioned reinforcer that in training required a single peck to produce because no required pecking would be more similar to a single peck than to 20 pecks. To allow for this possibility, Clement et al. presented three kinds of conditioned reinforcer probe trials: Trials initiated by a single peck to a white key, trials initiated by 20 pecks to a white key, and trials that started with a choice between the two conditioned reinforcers, with no white key.

The results of this experiment were clear. Regardless of the pecking requirement, on test trials (20, 1, or no pecks), the pigeons showed a significant preference (69.3%) for the conditioned stimulus that in training had required 20 pecks to produce. Thus, they showed a justification of effort effect. Furthermore, the two simultaneous discriminations were not acquired at different rates. That is, neither the number trials required to acquire the two simultaneous discriminations nor the number of reinforcements associated with the two S+ stimuli were significantly different.

A similar result was obtained by Kacelnik and Marsh (2002) with starlings. With their procedure, on some trials, they required the starlings to fly back and forth four times from one end of their cage to the other in order to light a colored key and peck the key to obtain a reinforcer. On other trials, the starlings had to fly back and forth 16 times to obtain a different colored key and peck the key to obtain the same reinforcer. On test trials, when the starlings were given a choice between the two colored lights without a flight requirement, 83% of them preferred the color that had required the greatest number of flights to produce.

Clement et al. (2000) and Kacelnik and Marsh (2002) used colors as the conditioned reinforcers to be able to use a common reinforcer as the outcome for both the easy and hard training trial. But in the natural ecology of animals, it is more likely that less arbitrary cues would be associated with the different alternatives. For example, one could ask if an animal might value reinforcement more from a particular location if it had to work harder to get the reinforcer from that location. In nature one could require that the animal travel farther to obtain food from one location than from another but it would be difficult to allow the animal to choose between the two locations without incurring the added cost of the additional travel time. However, such an experiment could be conducted in an operant chamber by manipulating the response requirement during training. Thus, we conducted an experiment in which we used two feeders, one that provided food on trials in which 30 pecks were required to the center response key, the other that provided the same food but at a different location, on trials in which a single peck was required to the center response key (Friedrich & Zentall, 2004). Prior to the start of training, we obtained a baseline feeder preference score for each pigeon. On each forced trial, the left or right key was illuminated (white) and pecks to the left key raised the left feeder, whereas pecks to the right key raised the right feeder. On interspersed choice trials, both the right and left keys were lit and the pigeons had a choice of which feeder would be raised (see Figure 2 top).

On training trials, the center key was illuminated (yellow) and either 1 peck or 30 pecks were required to turn off the center key and raised one of the two feeders. For each pigeon, the high-effort response raised the less preferred feeder and the low-effort response raised the more preferred feeder. Forced and free choice feeder trials continued through training to monitor changes in feeder preference (see Figure 2 bottom). Over the course of training, we found that there was a significant (20.5%) increase in preference for the originally non-preferred feeder (the feeder associated with the high-effort response; see Figure 3). To ensure that the increased preference for the originally non-preferred feeder was not due to the extended period of training, a control group was included. For the control group, over trials, each of the two response requirements was followed by each feeder equally often. Relative to the initial baseline preference, this group showed only a 0.5% increase in preference for their non-preferred feeder as a function of training. Thus, it appears that the value of the location of food can be enhanced by being preceded by a high-effort response, as compared to a low-effort response.

The ecological validity of the effect of prior effort on preference for the outcome that follows was further tested in a recent experiment by Johnson and Gallagher (2011) in which mice were trained to press one lever for glucose and a different lever for polycose. When initially tested, the mice showed a preference for the glucose; however, when the response requirement for the polycose was increased from one to 15 lever presses, and the mice were offered both reinforcers, they showed a preference for the polycose over the sucrose. Thus, increasing the effort required to obtain the less preferred food resulted in a reversal in preference. The once less-preferred food was now more preferred. Furthermore, neutral cues that had been paired with the reinforcers during training (a tone for one, white noise for the other) then became conditioned reinforcers that the mice worked to obtain in extinction, and they responded preferentially to produce the high-effort cue.

Had the experiments described above been conducted with human subjects, the results likely would have been attributed to cognitive dissonance. It is unlikely, however, that cognitive dissonance is responsible for the added value given to outcomes that follow greater effort in pigeons and mice. Instead, this phenomenon can be described more parsimoniously as a form of positive contrast.

A Model of Justification of Effort for Animals

To model the contrast account, one should set the relative value of the trial to zero. Next, it is assumed that key pecking (or the time needed to make those pecks) is a relatively aversive event and results in a negative change in the value of the trial. It is also assumed that obtaining the reinforcer causes a shift to a more positive value (relative to the value at the start of the trial). The final assumption is that the value of the reinforcer depends on the relative change in value; that is, the change in value from the end of the response requirement to the appearance of the reinforcer (or the appearance of the conditioned reinforcer that signals reinforcement; see Figure 4). In the case of the second experiment, it would be the change in value from the end of the response requirement to the location of the raised feeder. Thus, because the positive change in value following the high-effort response would be larger than the change in value following the low-effort response, the relative value of the reinforcer following a high-effort response should be greater than that of the low-effort response.

A similar model of suboptimal choice has been proposed by Aw, Vasconcelos, and Kacelnik (2011). They indicate “that animals may attribute value to their options as a function of the experienced fitness or hedonic change at the time of acting” (p. 1118). That is, the value of a reinforcer may depend on the state of the animal at the time of reinforcement. The poorer the state of the animal, the more valued the reinforcer will be. They have referred to this implied contrast as state-dependent valuation learning.

Relative Aversiveness of the Prior Event

Delay to Reinforcement as an Aversive Event.

If the interpretation of these experiments that is presented in Figure 4 is correct, then other relatively-aversive prior events (as compared with the comparable event on alternative trials) should result in a similar enhanced preference for the stimuli that follow. For example, given that pigeons should prefer a shorter delay to reinforcement over a longer delay to reinforcement, they should also prefer discriminative stimuli that follow a delay over those that follow no delay.

To test this hypothesis, we trained pigeons to peck the center response key (20 times on all trials) to produce a pair of discriminative stimuli (as in Clement et al., 2000). On some trials, pecking the response key was followed immediately by one pair of discriminative stimuli (no delay), whereas on the remaining trials, pecking the response key was followed by a different pair of discriminative stimuli but only after a delay of 6 sec. On test trials, the pigeons were given a choice between the two conditioned reinforcers, but in this experiment they showed no preference (DiGian, Friedrich, & Zentall, 2004, Group Unsignaled Delay).

One difference between the manipulation of effort used in the first two experiments and the manipulation of delay used in this one was in the effort manipulation in the earlier experiments. Once the pigeon had pecked once and the discriminative stimuli failed to appear, the pigeon could anticipate that 19 additional pecks would be required. Thus, the additional effort could be anticipated following the first response and the pigeon would be required to make 19 more responses in the presence of that anticipation. In the case of the delay manipulation, however, the pigeon could not anticipate whether a delay would occur or not, and at the time the delay occurred, no further responding was required. Thus, with the delay manipulation, the pigeon would not have to experience having to peck in the context of the anticipated delay. Would the results be different if the pigeon could anticipate the delay at a time when responding was required? To test this hypothesis, the delay to reinforcement manipulation was repeated but this time the initial stimulus was predictive of the delay (DiGian et al., 2004, Group Signaled Delay). On half of the trials, a vertical line appeared on the response key and 20 pecks resulted in the immediate appearance of a pair of discriminative stimuli (e.g., red and yellow). On the remaining trials, a horizontal line appeared on the response key and 20 pecks resulted in the appearance of the other pair of discriminative stimuli (e.g., green and blue) but only after a 6-sec delay (see Figure 5). For this group, the pigeons could anticipate whether 20 pecks would result in a delay or not, so they had to peck in the context of the anticipated delay. When pigeons in this group were tested, as in the effort-manipulation experiments, they showed a significant (65.4%) preference for the conditioned reinforcer that in training had followed the delay. Once again, the experience of a relatively-aversive event produced an increase in the value of the conditioned reinforcer that followed. Furthermore, the results of this experiment demonstrated that it may be necessary for the subject to anticipate the aversive event for positive contrast to be found.

The Absence of Reinforcement as an Aversive Event

A related form of relatively-aversive event is the absence of reinforcement in the context of reinforcement on other trials. Could reinforcement or its absence result in a preference for the conditioned reinforcer that follows the absence of reinforcement? To test this hypothesis, pigeons were trained to peck a response key five times on all trials to produce a pair of discriminative stimuli. On some trials pecking the response key was followed immediately by 2-s access to food from the central feeder and then immediately by the presentation of one pair of discriminative stimuli, whereas on the remaining trials pecking the response key was followed by the absence of food (for 2 s) and then by the presentation of a different pair of discriminative stimuli. On test trials, the pigeons were given a choice between the two S+ stimuli, but once again they showed no preference (Friedrich, Clement, & Zentall, 2005, Group Unsignaled Reinforcement).

As with the unsignaled delay condition, for this group, the aversive event, the absence of reinforcement, could not be anticipated prior to its occurrence. To test the hypothesis that this contrast effect depends on the anticipation of the aversive event, the absence of reinforcement manipulation was repeated but this time the initial stimulus was predictive of the delay (Friedrich et al., 2005, Group Signaled Reinforcement). Once again, on half of the trials, a vertical line appeared on the response key and 5 pecks resulted in the presentation of food followed by the appearance of one pair of discriminative stimuli. On the remaining trials, a horizontal line appeared on the response key and 5 pecks resulted in the absence of food followed by the appearance of the other pair of discriminative stimuli (see Figure 6). For this group, the pigeons could anticipate whether 5 pecks would result in reinforcement or not. When pigeons in this group were tested, they showed a significant (66.7%) preference for the conditioned reinforcer that in training had followed the absence of reinforcement. Once again, the experience of a relatively aversive event produced an increase in the value of the conditioned reinforcer that followed.

The Anticipation of Effort as the Aversive Event.

Can anticipated effort, rather than actual effort, serve as the aversive event that increases the value of stimuli signaling reinforcement that follows? This question addresses the issue of whether the positive contrast between the initial aversive event and the conditioned reinforcer depends on actually experiencing the aversive event. One account of the added value that accrues to stimuli that follow greater effort is that during training, the greater effort experienced produces a heightened state of arousal, and in that heightened state of arousal, the pigeons learn more about the discriminative stimuli that follow, than about the discriminative stimuli that follow the lower state of arousal produced by lesser effort. Examination of the acquisition functions for the two simultaneous discriminations offers no support for this hypothesis. Over the various experiments that we have conducted, there has been no tendency for the simultaneous discrimination that followed greater effort, longer delays, or the absence of reinforcement to have been acquired faster than the discrimination that followed less effort, shorter delays, or reinforcement. However, those discriminations were acquired very rapidly and there might have been a ceiling effect. That is, it might be easy to miss a small difference in the rate of discrimination acquisition sufficient to produce a preference for the conditioned reinforcer that follows the more aversive event.

Thus, the purpose of the anticipation experiments was to ask if we could obtain a preference for the discriminative stimuli that followed a signal that more effort might be required but actually was not required on that trial. More specifically, at the start of half of the training trials, pigeons were presented with, for example, a vertical line on the center response key. On half of these trials, pecking the vertical line replaced it with a white key and a single peck (low effort) to the white key resulted in reinforcement. On the remaining vertical-line trials, pecking the vertical line replaced it with a simultaneous discrimination S+L S-L on the left and right response keys and choice of the S+ was reinforced. A schematic presentation of the design of this experiment appears in Figure 7.

On the remaining training trials, the pigeons were presented with a horizontal line on the center response key. On half of these trials, pecking the horizontal line replaced it with a white key and 30 pecks (high effort) to the white key resulted in reinforcement. On the remaining horizontal-line trials, pecking the horizontal line replaced it with a different simultaneous discrimination S+H S-H on the left and right response keys and again choice of the S+ was reinforced. On test trials when the pigeons were given a choice between S+H and S+L, once again, they showed a significant (66.5%) preference for S+H.

It is important to note that in this experiment the events that occurred in training on trials, involving the two pairs of discriminative stimuli, were essentially the same. It was only on the other half of the trials, those trials on which the discriminative stimuli did not appear, that differential responding was required. Thus, the expectation of differential effort, rather than actual differential effort appears to be sufficient to produce a differential preference for the conditioned reinforcers that follow. These results extend the findings of the earlier research to include anticipated effort.

The Anticipation of the Absence of Reinforcement as the Aversive Event.

If anticipated effort can function as a relative conditioned aversive event, can the anticipated absence of reinforcement serve the same function? Using a design similar to that used to examine differential anticipated effort, we evaluated the effect of differential anticipated reinforcement (Clement & Zentall, 2002, Exp. 2). On half of the training trials, pigeons were presented with a vertical line on the center response key. On half of these trials, pecking the vertical line was followed immediately by reinforcement (high probability reinforcement). On the remaining vertical-line trials, pecking the vertical line replaced it with a simultaneous discrimination S+HP S-HP and choice of the S+ was reinforced, but only on a random 50% of the trials. A schematic presentation of the design of this experiment appears in Figure 8. On the remaining training trials, the pigeons were presented with a horizontal line on the center response key. On half of these trials, pecking the horizontal line was followed immediately by the absence of reinforcement (low probability reinforcement). On the remaining horizontal-line trials, pecking the horizontal line replaced it with a different simultaneous discrimination S+LP S-LP and again choice of the S+ was reinforced, but again, only on a random 50% of the trials. On test trials, when the pigeons were given a choice between S+HP and S+LP, they showed a significant (66.9%) preference for S+LP. Thus, the anticipation of an aversive, absence-of-food event appears to produce a preference for the S+ that follows the initial stimulus and that preference is similar to the anticipation of a high effort response.

In a follow-up experiment (Clement & Zentall, 2002, Exp. 3), we tried to determine whether preference for the discriminative stimuli associated with the anticipation of the absence of food was produced by the anticipation of positive contrast between the certain absence of food and a 50% chance of food (on discriminative stimulus trials) or negative contrast between the certain anticipation of food and a 50% chance of food (on the other set of discriminative stimulus trials). A schematic presentation of the design of this experiment appears at the top of Figure 9.

For Group Positive, the conditions of reinforcement were essentially nondifferential (i.e., reinforcement always followed vertical-line trials whether the discriminative stimuli S+HP S-HP were presented or not). Thus, on half of the vertical line trials, reinforcement was presented immediately for responding to the vertical line. On the remaining vertical-line trials, pecking the vertical line replaced it with a different simultaneous discrimination S+HP S-HP and reinforcement was presented for responding to the S+. Thus, there should have been little contrast established between these two kinds of trial.

On half of the horizontal-line trials, however, no reinforcement always followed responses to the horizontal line. On the remaining horizontal-line trials involving S+LP S-LP, reinforcement was presented for responding to the S+. Thus, for this group, on horizontal-line trials, there was the opportunity for positive contrast to develop on discriminative stimulus trials (i.e., the pigeons should expect that reinforcement might not occur on those trials and they might experience positive contrast when it does occur).

For Group Negative, on all horizontal-line trials the conditions of reinforcement were essentially nondifferential (i.e., the probability of reinforcement on horizontal-line trials was always 50% whether the trials involved discriminative stimuli or not). Thus, there should have been little contrast established between these two kinds of trial (see the bottom of Figure 9). That is, on half of the horizontal-line trials, reinforcement was provided immediately with a probability of .50 for responding to the horizontal line. On the remaining horizontal-line trials, the discriminative stimuli S+LP S-LP were presented and reinforcement was obtained for choices of the S+ but only on 50% of the trials.

On half of the vertical-line trials, however, reinforcement was presented immediately for responding to the vertical line (with a probability of 1.00). On the remaining vertical-line trials, the discriminative stimuli S+LP S-LP were presented and reinforcement was provided for choice of the S+ with a probability of 50%. Thus, for this group, on verticalline trials, there was the opportunity for negative contrast to develop on discriminative stimulus trials (i.e., the pigeons should expect that reinforcement is quite likely and they might experience negative contrast when it does not occur).

On test trials, when pigeons in Group Positive were given a choice between the two S+ stimuli, they showed a significant (60.1%) preference for the positive discriminative stimulus that in training was preceded by a horizontal line (the initial stimulus that on other trials was followed by the absence of reinforcement). Thus, Group Positive showed evidence of positive contrast.

When pigeons in Group Negative were given a choice between the two S+ stimuli, they showed a 58.1% preference for the positive discriminative stimulus that in training was preceded by a horizontal line (the initial stimulus that on other trials was followed by a lower probability of reinforcement than on comparable trials involving the vertical line). Thus, Group Negative showed evidence of negative contrast. In this case, it should be described as a reduced preference for the positive discriminative stimulus preceded by the vertical line, which on other trials was associated with a higher probability of reinforcement (100%). Considering the results from both Group Positive and Group Negative it appears that both positive and negative contrast contributed to the preferences found by Clement and Zentall, (2002, Exp. 2).

Hunger as the Aversive Event

According to the contrast model, if pigeons are trained to respond to one conditioned reinforcer when hungry and to respond to a different conditioned reinforcer when less hungry, when they are given a choice between the two conditioned reinforcers, they should prefer the conditioned reinforcer to which they learned to respond when hungrier. That is, they should prefer the stimulus that they experienced when they were in a relatively more aversive state. Vasconcelos and Urcuioli (2008b) tested this prediction by training pigeons to peck one colored stimulus on days when they were hungry and to peck a different colored stimulus on days when they were less hungry. On test days, when the pigeons were given a choice between the two colored stimuli, they showed a preference for the stimulus that they pecked when they were hungrier. Furthermore, this effect was not state-dependent because the pigeons preferred the color that they had learned to peck when hungrier, whether they were tested more or less hungry. Similar results were reported by Marsh, Schuck-Paim, and Kacelnik (2004) with starlings (see also Pompilio & Kacelnik, 2005). Furthermore, the effect appears to have considerable generality because Pompilio, Kacelnik, and Behmer (2006) were able to show similar effects in grasshoppers.

Within-Trial Contrast in Humans.

It can be argued that if within-trial contrast is analogous to justification of effort, one should be able to show similar effects with humans. In fact, when humans were given a modified version of the task used by Clement et al. (2000) a similar effect was found (Klein, Bhatt, & Zentall, 2005). The humans were told that they would have to “click on a mouse” to receive a pair of abstract shapes and by clicking on the shapes they could learn which shape was correct. On some trials, a single click was required to present one of two pairs of shapes and one shape from each pair was designated as correct. On the remaining trials, 20 clicks were required to present one of two different pairs of shapes and again one shape from each pair was designated as correct. Thus, there was a total of four pairs of shapes. On test trials, the subjects were asked to choose between pairs of correct shapes, one shape that had followed a single mouse click the other that had followed 20 mouse clicks. Consistent with the contrast hypothesis, subjects showed a significant (65.2%) preference for the shapes that followed 20 clicks. Furthermore, after their choice, when the subjects were asked why they had chosen those shapes, typically they did not know and most of them were not even aware of which shapes had followed the large and small response requirement. When a similar procedure was used with 8-year old children, they showed a similar 66.7% preference for the shapes that they had to work harder to obtain (Alessandri, Darcheville, & Zentall, 2008).

Contrast or Relative Delay Reduction?

We have described the preferences we have found for conditioned reinforcers (and feeder location) as a contrast effect. However, one could also interpret these effects in terms of relative delay reduction (Fantino & Abarca, 1985). According to the delay reduction hypothesis, any stimulus that predicts reinforcement sooner in its presence than in its absence will become a conditioned reinforcer. In the present experiments, the temporal relation between the conditioned reinforcers and the reinforcers was held constant, so one could argue that neither conditioned reinforcer should have served to reduce the delay to reinforcement more than the other. But the delay reduction hypothesis is meant to be applied to stimuli in a relative sense. That is, one can consider the predictive value of the discriminative stimuli relative to the time in their absence or, in the present case, to the total duration of the trial. If one considers delay reduction in terms of its duration relative to the duration of the entire trial, then the delay reduction hypothesis can account for the results of the present experiments. For example, in the case of the differential effort manipulation, as it takes longer to produce 20 responses (pecks or clicks) than to produce 1 response, 20-response trials would be longer in duration than 1-response trials. Thus, the appearance of the discriminative stimuli would occur relatively later in a 20-response trial than in a 1-response trial. The later in a trial that the discriminative stimuli appear, the closer would be their onset to reinforcement, relative to the start of the trial and thus, the greater relative reduction in delay that they would represent.

The delay reduction hypothesis can also account for the effect seen with a delay versus the absence of a delay. But what about trials with reinforcement versus trials without reinforcement? In this case, the duration of the trial is the same with and without reinforcement, prior to the appearance of the discriminative stimuli; however, delay reduction theory considers the critical time to be the interval between reinforcements. Thus, on trials in which the discriminative stimuli are preceded by reinforcement, the time between reinforcements is short, so the discriminative stimuli are associated with little delay reduction. On trials in which the discriminative stimuli are preceded by the absence of reinforcement, however, the time between reinforcements is relatively long (i.e., the time between reinforcement on the preceding trial and reinforcement on the current trial), so the discriminative stimuli on the current trial would be associated with a relatively large reduction in delay.

Delay reduction theory has a more difficult time accounting for the effects of differential anticipated effort because trials with both sets of discriminative stimuli were not differentiated by number of responses, delay, or reinforcement. Thus, all trials with discriminative stimuli should be of comparable duration. The same is true for the effects of differential anticipated reinforcement because that manipulation occurred on trials independent of the trials with the discriminative stimuli. Thus, taken as a whole, based on what has been presented to this point, the contrast account appears to offer a more parsimonious account of the data.

On the other hand, it should be possible to distinguish between the delay reduction and contrast accounts with the use of a design similar to that used in the first experiment, with one important change. Instead of requiring that the pigeons peck many times on half of the trials and a few times on the remaining trials, one could use two schedules that accomplish the same thing while holding the duration of the trial event constant. This could be accomplished by using a fixed interval schedule (FI, the first response after a fixed duration would present one pair of discriminative stimuli) on half of the trials and a differential reinforcement of other behavior schedule (DRO, the absence of key pecking for the same fixed duration would present the other pair of discriminative stimuli) on the remaining trials. Assuming that the pigeons prefer the DRO schedule (but it is not certain that they would), then according to the contrast account the pigeons should prefer the discriminative stimuli that follow the FI schedule over the discriminative stimuli that follow the DRO schedule. According to the delay reduction hypothesis, if trial duration is held constant and the two pairs of discriminative stimuli occupy the same relative proportion of the two kinds of trial, the pigeons should not differentially prefer either pair of discriminative stimuli, regardless of which schedule is preferred.

We tested the prediction of delay reduction theory by equating the trial duration on high effort and low effort trials by first training the pigeons to respond on a FI schedule to one stimulus on half of the trials and a DRO schedule to a different stimulus on the remaining trials (Singer, Berry, & Zentall, 2007). But before introducing the discriminative stimuli, we tested the pigeons for their schedule preference. We then followed the two schedules with discriminative stimuli as in the earlier research and finally tested the pigeons for their conditioned reinforcer preference (see Figure 10). Consistent with contrast theory, we found that the pigeons reliably preferred (by 63.2%) the discriminative stimuli that followed their least preferred schedule (Figure 11; see also Singer & Zentall, 2011, Exp. 1). Furthermore, consistent with a contrast account, as the schedule preference varied in direction and degree among the pigeons, we examined the correlation between schedule preference and preference for the conditioned reinforcer that followed and found a significant negative correlation (r = -.78). The greater the schedule preference the less they preferred the conditioned reinforcer that followed that schedule.

Further support for the contrast account came from an experiment in which there were 30-peck trials and single-peck trials but trial duration was extended on single-peck trials to equal the duration of 30-peck trials by inserting a delay following the single peck, equal to the time each pigeon took to complete the immediately-preceding 30-peck requirement (Singer & Zentall, 2011, Exp. 2). Once again, following a test to determine which schedule was preferred, discriminative stimuli were inserted following completion of the schedule and the pigeons’ preference for the conditioned reinforcers was assessed. Again, the pigeons preferred the conditioned reinforcer that followed the least-preferred schedule, 60.4% of the time (but see Vasconcelos, Lionello-DeNolf, & Urcuioli, 2007).

A different approach to equating trial duration was demonstrated with human subjects by Alessandri, Darcheville, Delevoye-Turrell, and Zentall (2008). Instead of using number of mouse clicks as the differential initial event, we used pressure on a transducer. On some trials, signaled by a discriminative stimulus, the subjects had to press the transducer lightly to produce a pair of shapes. On other trials, signaled by a different discriminative stimulus, the subjects had to press the transducer with greater force (50% of their maximum force assessed during pretraining). Following training, when subjects were given a choice between pairs of the conditioned reinforcers, they showed a significant 66.7% preference for those stimuli that had required the greater force to produce in training (and the effect was independent of the force required on test trials). Thus, further support for the contrast account was obtained under conditions in which it would be difficult to account for the effect by delay reduction theory.

Failures to Replicate the Within-Trial Contrast Effect

Several studies have reported a failure to obtain a contrast effect of the kind reported by Clement et al. (2000). Such reports are instructive because they can help to identify the boundary conditions for observing the effect. The first of these studies was reported by Vasconcelos, Urcuioli, and Lionello-DeNolf (2007) who attempted to replicate the original Clement et al. finding with 20 sessions of training beyond acquisition of the simple simultaneous discriminations that were acquired very quickly. It should be noted, however, that in more-recent research we have found that the amount of training required to establish the within-trial contrast effect is often greater than that used by Clement et al. Although Clement et al. found a contrast effect with 20 sessions of additional training, later research suggested that up to 60 sessions of training is often required to obtain the effect (see, e.g., Friedrich & Zentall, 2004).

Arantes and Grace (2007) also failed to replicate the contrast effect. In their first experiment they tested their pigeons without overtraining and in their second experiment they tested their pigeons at various points up to 27 sessions of overtraining. Thus, once again it may be that insufficient training was provided. However, in their second experiment, a subgroup of four pigeons was given more than twice the number of training sessions and although they did find a preference for the conditioned reinforcer that followed the greater effort in training, it was not statistically reliable. However, the smaller contrast effect reported by Arantes and Grace may be attributable to the extensive prior experience (in a previous experiment) that these pigeons had had with lean variable interval schedules. It is possible that the prior experience with lean schedules sufficiently reduced the aversiveness of the 20-peck requirement to reduce the magnitude of the contrast effect that they found. Another factor that may have contributed to the reduced magnitude of their effect was the use of a 6-s delay between choice of the conditioned reinforcer and reinforcement. Although Clement et al. (2000) also included a 6-s delay, later research suggested that contrast effects at least as large can be obtained if reinforcement immediately follows choice of the conditioned reinforcer.

Finally, Vasconcelos and Urcuioli (2008a) noted that they too failed to find a significant contrast effect following extensive overtraining. However, the effect that they did find (about 62% choice of the conditioned reinforcer that followed the greater pecking requirement) was quite comparable in magnitude to the effect reported by Clement et al. (2000). Their failure to find a significant effect may be attributed to the fact that there were only four pigeons in their experiment. That is, their study may have lacked sufficient power to observe significant within-trial contrast. Thus, the several failures to find a contrast effect with procedures similar to those used by Clement et al. suggest that observation of the contrast effect may require considerable overtraining, the absence of prior training with lean schedules of reinforcement, and a sufficient sample size to deal with individual differences in the magnitude of the effect.

The Nature of the Contrast

The contrast effects found in the present research appear to be somewhat different from the various forms of contrast that have been reported in the literature (see Flaherty, 1996). Flaherty distinguishes among three kinds of contrast.

Incentive Contrast

In incentive contrast, the magnitude of reward that has been experienced for many trials, suddenly changes, and the change in behavior that follows is compared with the behavior of a comparison group that has experienced the final magnitude of reinforcement from the start. Early examples of incentive contrast were reported by Tinklepaugh (1928), who found that if monkeys were trained for a number of trials with a preferred reward (e.g., fruit), when they then encountered a less preferred reward (e.g., lettuce, a reward that they would normally readily work for) they often would refuse to eat it.

Incentive contrast was more systematically studied by Crespi (1942, see also Mellgren, 1972). Rats trained to run down an alley for a large amount of food and shifted to a small amount of food, typically run slower than rats trained to run for the smaller amount of food from the start (negative incentive contrast). Conversely, rats trained to run for a small amount of food and shifted to a large amount of food may run faster than rats trained to run for the larger amount of food from the start (positive incentive contrast). By its nature, incentive contrast must be assessed following the shift in reward magnitude rather than in anticipation of the change because, generally, only a single shift is experienced.

Capaldi (1972) has argued that negative successive incentive contrast of the kind studied by Crespi (1942) can be accounted for as a form of generalization decrement (the downward shift in incentive value represents not only a shift in reinforcement value but also a change in context), however, generalization decrement is not able to account for positive successive incentive contrast effects (also found by Crespi and in the present research) when the magnitude of reinforcement increases.

Incentive contrast would seem to be an adaptive mechanism by which animals can increase their sensitivity to changes in reinforcement density. Just as animals use lateral inhibition in vision to help them discriminate spatial changes in light intensity resulting in enhanced detection of edges (or to provide better figure-ground detection), so too may incentive contrast help the animal detect changes in reinforcement magnitude important to its survival. Thus, incentive contrast may be a perceptually-mediated detection process.

Anticipatory Contrast

In a second form of contrast, anticipatory contrast, there are repeated (typically one a day) experiences with the shift in reward magnitude, and the measure of contrast involves behavior that occurs prior to the anticipated change in reward value. Furthermore, the behavior assessed is typically consummatory behavior rather than running speed. For example, rats often drink less of a weak saccharin solution if they have learned that it will be followed by a strong sucrose solution, relative to a control group for which saccharin is followed by more saccharin (Flaherty, 1982). This form of contrast differs from others in the sense that the measure of contrast involves differential rates of the consumption of a reward (rather than an independent behavior such as running speed).

Behavioral Contrast

A third form of contrast involves the random alternation of two signaled outcomes. When used in a discrete-trials procedure with rats, the procedure has been referred to as simultaneous incentive contrast. Bower (1961), for example, reported that rats trained to run down an alley to both large and small signaled magnitudes of reward ran slower to the small magnitude of reward than rats that ran only to the small magnitude of reward.

The more-often-studied, free-operant analog of this task is called behavioral contrast. To observe behavioral contrast, pigeons are trained on an operant task involving a multiple schedule of reinforcement. In a multiple schedule, two (or more) schedules, each signaled by a distinctive stimulus, are randomly alternated. Positive behavioral contrast can be demonstrated by training pigeons initially with equal probability of reinforcement schedules (e.g., two variable-interval 60-s schedules) and then reducing the probability of reinforcement in one schedule (e.g., from variable-interval 60-s to extinction) and noting an increase in the response rate in the other, unaltered schedule (Halliday & Boakes, 1971; Reynolds, 1961). Similar results can be demonstrated in a between groups design (Mackintosh, Little, & Lord, 1972) in which pigeons are trained on the multiple variable-interval 60-s and extinction schedules from the start, and their rate of pecking during the variable-interval 60-s schedule is compared with other pigeons that have been trained on two variable-interval 60-s schedules.

The problem with classifying behavioral contrast according to whether it involves a response to entering the richer schedule (as with incentive contrast) or the anticipation of entering the poorer schedule (as with anticipatory contrast) is, during each session, there are multiple transitions from the richer to the poorer schedule and vice versa. Thus, when one observes an increase in responding in the richer schedule resulting from the presence of the poorer schedule at other times, it is not clear whether the pigeons are reacting to the preceding poorer schedule or they are anticipating the next poorer schedule.

Williams (1981) attempted to distinguish between these two mechanisms by presenting pigeons with triplets of trials in a ABA design (with the richer schedule designated as A) and comparing their behavior to that of pigeons trained with an AAA design. Williams found very different kinds of contrast in the two A components of the ABA schedule. In the first A component, Williams found a generally higher level of responding that was maintained over training sessions (see also Williams, 1983). In the second A component, however, he found a higher level of responding primarily at the start of the component, an effect known as local contrast, but the level of responding was not maintained over training sessions (see also, Cleary, 1992). Thus, there is evidence that behavioral contrast may be attributable primarily to the higher rate of responding by pigeons in anticipation of the poorer schedule rather than in response to the appearance of the richer schedule (Williams, 1981; see also Williams & Wixted, 1986).

It is generally accepted that the higher rate of responding to the stimulus associated with the richer schedule of reinforcement occurs because, in the context of the poorer schedule, that stimulus is relatively better at predicting reinforcement (Keller, 1974). Or in more cognitive terms, the richer schedule seems even better in the context of a poorer schedule.

There is evidence, however, that it is not that the richer schedule appears better, but that the richer schedule will soon get worse. In support of this distinction, although pigeons peck at a higher rate at stimuli that predict a worsening in the probability of reinforcement, it has been found that when given a choice, pigeons prefer stimuli that they respond to less but that predict no worsening in the probability of reinforcement (Williams, 1992). Thus, curiously, under these conditions, response rate has been found to be negatively correlated with choice.

The implication of this finding is that the increased responding associated with the richer schedule does not reflect its greater value to the pigeon, but rather its function as a signal that conditions will soon get worse because the opportunity to obtain reinforcement will soon diminish. This analysis suggests that the mechanism responsible for anticipatory contrast (Flaherty, 1982) and, in the case of behavioral contrast, responding in anticipation of a worsening schedule (Williams, 1981), is likely to be a compensatory or learned response. In this sense, these two forms of contrast are probably quite different from the perceptual-like detection process involved in incentive contrast.

The Present Within-Trial Contrast Effect

What all contrast effects have in common is the presence, at other times, of a second condition that is either better or worse than the target condition. The effect of the second condition often is to exaggerate the difference between the two conditions. Although there have been attempts to account for these various contrast effects, Mackintosh (1974) concluded that no single principle will suffice (see also Flaherty, 1996). Thus, even before the contrast effect reported by Clement et al. (2000) and presented here was added to the list, contrast effects resisted a comprehensive explanation.

Procedurally, the positive contrast effect reported by Clement et al. (2000) appears to be most similar to that involved in anticipatory contrast (Flaherty, 1982) because in each case there is a series of paired events, the second of which is better than the first. High effort is followed by discriminative stimuli in the case of the Clement et al. procedure, and a low concentration of saccharin is followed by a higher concentration of sucrose in the case of anticipatory contrast. However, the effect reported by Clement et al. is seen in a choice response made in the presence of the second event (i.e., preference for one conditioned reinforcer over the other) rather than the first (i.e., differential consumption of the saccharin solution).

Alternatively, although successive incentive contrast and the contrast effect reported by Clement et al. (2000) both involve a change in behavior during the second component of the task, the mechanisms responsible for these effects must be quite different. In the case of the Clement et al. procedure, the pigeons experienced the two-event sequences many hundreds of times prior to test and thus, they could certainly learn to anticipate the appearance of the discriminative stimuli and the reinforcers that followed, whereas in the case of successive incentive contrast, the second component of the task could not be anticipated.

The temporal relations involved in the within-trial contrast effect reported by Clement et al. (2000) would seem more closely related to those that have been referred to as local contrast (Terrace, 1966). As already noted, local contrast refers to the temporary change in response rate that occurs following a stimulus change that signals a change in schedule. But local contrast effects tend to occur early in training and they generally disappear with extended training. Furthermore,  Furthermore, if local contrast was responsible for the contrast effect reported by Clement et al., they should have found a higher response rate to the positive stimulus that followed the higher effort response than to the positive stimulus that followed the lower effort response. But differences in response rate have not been found, only differences in choice. Thus, the form of contrast characteristic of the research described in this review appears to be different from the various contrast effects described in the literature. First, the present contrast effect is a within-subject effect that is measured by preference score. Second, in a conceptual sense, it is the reverse of what one might expect based on more-typical contrast effects. Typically, a relatively-aversive event (e.g., delay to reinforcement) is judged to be more aversive (as measured by increased latency of response or decreased choice) when it occurs in the context of a less-aversive event that occurs on alternative trials (i.e., it is a between-trials effect). The contrast effect described here is assumed to occur within trials and the effect is to make the events that follow the relatively aversive event more preferred than similar events that follow less-aversive events. Thus, referring to this effect as a contrast effect is descriptive but it is really quite different from the other contrast effects described by Flaherty (1996). For all of the above reasons we consider the contrast effect presented here to be different from other contrast effects that have been studied in the literature and we propose to refer to it as within-trial contrast.

Possibly Related Psychological Phenomena

The within-trial contrast effect described here may be related to other psychological phenomena that have been described in the literature.

Contrafreeloading.

A form of contrast similar to that found in the present experiment may be operating in the case of the classic contrafreeloading effect (e.g., Carder & Berkowitz, 1970; Jensen, 1963; Neuringer, 1969). For example, pigeons trained to peck a lit response key for food will often obtain food by pecking the key even when they are presented with a dish of free food. Although it is possible that other factors contribute to the contrafreeloading effect (e.g., reduced familiarity with the free food in the context of the operant chamber, Taylor, 1975, or perhaps preference for small portions of food spaced over time), it is also possible that the pigeons value the food obtained following the effort of key pecking more than the free food, and if the effort required is relatively small, the added value of food for which they have to work may at times actually be greater than the cost of the effort required to obtain it.

Justification of Effort.

As mentioned earlier, justification of effort in humans has been attributed to the discrepancy between one’s beliefs and one’s behavior (Aronson & Mills, 1959). The present research suggests that contrast may be a more parsimonious interpretation of this effect not only in pigeons but also in humans. In fact, the present results may have implications for a number of related phenomena that have been studied in humans.

The term work ethic has often been used in the human literature to describe a value or a trait that varies among members of a population as an individual difference (e.g., Greenberg 1977). But it also can be thought of as a typically human characteristic that appears to be in conflict with traditional learning theory (Hull, 1943). Work (effort) is generally viewed as at least somewhat aversive and as behavior to be reduced, especially if less-effortful alternatives are provided to obtain a reward. Other things being equal, less work should be preferred over more work (and in general it is).Yet, it is also the case that work, per se, is often valued in our culture. Students are often praised for their effort independent of their success. Furthermore, the judged value of a reward may depend on the effort that preceded it. For example, students generally value a high grade that they have received in a course not only for its absolute value, but also in proportion to the effort required to obtain it. Consider the greater pride that a student might feel about an A grade in a difficult course (say, Organic Chemistry) as compared to a similar A grade in an easier course (say, Introduction to Golf).

Although, in the case of such human examples, cultural factors, including social rewards, may be implicated, a more fundamental, nonsocial mechanism may also be present. In the absence of social factors, it may generally be the case (as in the present experiments) that the contrast between the value of the task prior to reward and at the time of reward may be greater following greater effort than following less
effort.

Cognitive Dissonance.

As described earlier, when humans experience a tedious task, their evaluation of the aversiveness of the task is sometimes negatively correlated with the size of the reward provided for agreeing to describe the task to others as pleasurable, a cognitive dissonance effect (Festinger & Carlsmith, 1959). The explanation that has been given for the cognitive dissonance effect is that the conflict between attitude (the task was tedious) and behavior (participants had agreed to describe the task to another person as enjoyable) was more easily resolved when a large reward was given (“I did it for the large reward”) and thus, a more honest evaluation of the task could be provided. However, one could also view the contrast between effort and reward to be greater in the large reward condition than in the small reward condition. Thus, in the context of the large reward, looking back at the subjective aversiveness of the prior task, it might be judged as greater than in the context of small reward.

Intrinsic versus Extrinsic Reinforcement.

Contrast effects of the kind reported here may also be responsible for the classic finding that extrinsic reinforcement may reduce intrinsic motivation (Deci, 1975; but see also Eisenberger & Cameron, 1996). If rewards are given for activities that may be intrinsically rewarding (e.g., puzzle solving), providing extrinsic rewards for such an activity may lead to a subsequent reduction in that behavior when extrinsic rewards are no longer provided. This effect has been interpreted as a shift in self-determination or locus of control (Deci & Ryan,
1985; Lepper, 1981). But such effects can also be viewed as examples of contrast. In this case, it may be the contrast between extrinsic reinforcement and its sudden removal that is at least partly responsible for the decline in performance (Flora, 1990). Such contrast effects are likely to be quite different from those responsible for the results of the present experiment, however, because the removal of extrinsic reinforcement results in a change in actual reward value, relative to the reward value expected (i.e., the shift from a combination
of both extrinsic and intrinsic reward to intrinsic reward alone). Thus, the effect of extrinsic reinforcement on intrinsic motivation is probably more similar to traditional reward shift effects of the kind reported by Crespi (1942, i.e., rats run slower after they have been shifted from a large to a small magnitude of reward than rats that have always experienced the small magnitude of reward).

Learned Industriousness.

Finally, contrast effects may also be involved in a somewhat different phenomenon that Eisenberger (1992) has called learned industriousness. Eisenberger has found that if one is rewarded for putting a large amount of effort into a task (compared to a small amount of effort into a task), it may increase ones general readiness to expend effort in other goal-directed tasks. Eisenberger has attributed this effect to the conditioned reward value of effort, a reasonable explanation for the phenomenon, but contrast may also be involved.

Depending on the relative effort required in the first and second tasks, two kinds of relative contrast are possible. First, if the target (second) task is relatively effortful, negative contrast between the previous low-effort task and the target task may make persistence on the second task more aversive for the low-effort group (and the absence of negative contrast less aversive for the high-effort group). Second, for the high-effort group, if the target task requires relatively little effort, positive contrast between the previous high-effort
task and the target task may make persistence less aversive. In either case, contrast provides a reasonable alternative account of these data.

Conclusions

From the previous discussion it should be clear that positive contrast effects of the kind reported in the present research may contribute to a number of experimental findings that have been reported using humans (and sometimes animals) but that traditionally have been explained using more complex cognitive and social accounts. Further examination of these phenomena from the perspective of simpler contrast effects may lead to more parsimonious explanations of what have previously been interpreted to be uniquely human phenomena.

But even if contrast is involved in these more complex phenomena, it may be that more cognitive factors, of the type originally proposed, may also play a role in these more complex social contexts. It would be informative, however, to determine the extent to which contrast effects contribute to these phenomena.

Finally, the description of the various effects as examples of contrast may give the mistaken impression that such effects are simple and are well understood. As prevalent as contrast effects appear to be, the mechanisms that account for them remain quite speculative. Consider the prevalence of the opposite effect, generalization, in which experience with one value on a continuum spreads to other values in direct proportion to their similarity to the experienced value (Hull, 1943). According to a generalization account, generalization between two values of reinforcement should tend to make the two values more similar to each other, rather than more different. An important goal of future research should be to identify the conditions that produce contrast and those than produce generalization.

At the very least, the presence of contrast implies some form of relational learning that cannot be accounted for by means of traditional behavioral theories. Thus, although contrast may provide an alternative, more parsimonious account of several complex social psychological phenomena, contrast should not be considered a simple mechanism. Instead it can be viewed as a set of relational phenomena that must be explained in their own right.

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Neurobiological Foundations of an Attribute Model of Memory

Raymond Kesner
University of Utah

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Abstract

Memory is a complex phenomenon due to a large number of potential interactions that are associated with the organization of memory at the psychological and neural system level. In this review article a tripartite, multiple attribute, multiple process memory model with different forms of memory and its neurobiological underpinnings is represented in terms of the nature, structure or content of information representation as a set of different attributes including language, time, place, response, reward value (affect) and visual object as an example of sensory-perception. For each attribute, information is processed in the event-based, knowledge-based, and rule-based memory systems through multiple operations that involve multiple neural underpinnings. Of the many processes associated with the event-based memory system, the emphasis will be placed on short-term or working memory and pattern separation. Of the many processes associated with the knowledgebased memory system, the emphasis will be placed on perceptual processes. Of the many processes associated with the rule-based memory system the emphasis will be on short-term or working memory and paired associate learning. For all three systems data will be presented to demonstrate differential neuroanatomical mediation and where available parallel results will be presented in rodents, monkeys and humans.

Keywords

event-based memory, knowledge-based memory, rule-based memory, attribute memory model, hippocampus, amygdala, caudate nucleus, perirhinal cortex, prefrontal cortex, parietal cortex and TE2 cortex

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Introduction

The structure and utilization of memory is central to one’s knowledge of the past, interpretation of the present, and prediction of the future. Therefore, the understanding of the structural and process components of memory systems at the psychological and neurobiological level is of paramount importance. There have been a number of attempts to divide learning and memory into multiple memory systems. Schacter & Tulving (1994) have suggested that one needs to define memory systems in terms of the kind of information to be represented, the processes associated with the operation of each system, and the neurobiological substrates including neural structures and mechanisms that subserve each system. Furthermore, it is likely that within each system, there are multiple forms or subsystems associated with each memory system and there are likely to be multiple processes that define the operation of each system. Finally, there are probably multiple neural structures that form the overall substrate of a memory system.

Currently, the most established models of memory can be characterized as dual memory system models with an emphasis on the hippocampus or medial temporal lobe for one component of the model and a composite of other brain structures as the other component. For example, Squire (1994) and Squire et al.(2004) have proposed that memory can be divided into a medial temporal-lobe-dependent declarative memory which provides for conscious recollection of facts and events and a non-hippocampal dependent nondeclarative memory which provides for memory without conscious access for skills, habits, priming, simple classical conditioning and non-associative learning. Others have used different terms to reflect the same type of distinction, including a hippocampal dependent explicit memory versus a non-hippocampal dependent implicit memory (Schacter, 1987), and a hippocampal dependent declarative memory based on the representation of relationships among stimuli versus a non-hippocampal dependent procedural memory based on the representation of a single stimulus or configuration of stimuli (Cohen & Eichenbaum, 1993; Eichenbaum, 2004). Olton (1983) has suggested a different dual memory system in which memory can be divided into a hippocampal dependent working memory defined as memory for the specific, personal, and temporal context of a situation and a non-hippocampal dependent reference memory, defined as memory for rules and procedures (general knowledge) of specific situations. Different terms have been used to reflect the same distinction including episodic versus semantic memory (Tulving, 1983).

However, memory is more complex and involves many neural systems in addition to the hippocampus. To remedy this situation, Kesner (2002) has proposed a tripartite attribute based theoretical model of memory which is organized into event-based, knowledge-based, and rule-based memory systems. Each system is composed of the same set of multiple attributes or forms of memory, characterized by a set of process oriented operating characteristics and mapped onto multiple neural regions and interconnected neural circuits (for more detail see Kesner 1998, 2002).

On a psychological level (see Tables 1, 2, 3), the eventbased memory system provides for temporary representations of incoming data concerning the present, with an emphasis upon data and events that are usually personal or egocentric and that occur within specific external and internal contexts. The emphasis is upon the processing of new and current information. During initial learning, great emphasis is placed on the event-based memory system, which will continue to be of importance even after initial learning in situations where unique or novel trial information needs to be remembered. This system is akin to episodic memory (Tulving, 1983) and some aspects of declarative memory (Squire, 1994).

The knowledge-based memory system provides for more permanent representations of previously stored information in long-term memory and can be thought of as one’s general knowledge of the world. The knowledge-based memory system would tend to be of greater importance after a task has been learned, given that the situation is invariant and familiar. The organization of these attributes within the knowledge-based memory system can take many forms and are organized as a set of attribute-dependent cognitive maps and their interactions, that are unique for each memory. This system is akin to semantic memory (Tulving, 1983).

The rule-based memory system receives information from the event-based and knowledge-based systems and integrates the information by applying rules and strategies for subsequent action. In most situations, however, one would expect a contribution of all three systems with a varying proportion of involvement of one relative to the other.The three memory systems are composed of the same forms, domains, or attributes of memory. Even though there could be many attributes, the most important attributes include space, time, response, sensory-perception, and reward value (affect). In humans a language attribute is also added. A spatial (space) attribute within this framework involves memory representations of places or relationships between places. It is exemplified by the ability to encode and remember spatial maps and to localize stimuli in external space. Memory representations of the spatial attribute can be further subdivided into specific spatial features including allocentric spatial distance, egocentric spatial distance, allocentric direction, egocentric direction, and spatial location. A temporal (time) attribute within this framework involves memory representations of the duration of a stimulus and the succession or temporal order of temporally separated events or stimuli, and from a time perspective, the memory representation of the past. A response attribute within this framework involves memory representations based on feedback from motor responses (often based on proprioceptive and vestibular cues) that occur in specific situations as well as memory representations of stimulus-response associations. A reward value (affect) attribute within this framework involves memory representations of reward value, positive or negative emotional experiences, and the associations between stimuli and rewards. A sensory-perceptual attribute within this framework involves memory representations of a set of sensory stimuli that are organized in the form of cues as part of a specific experience. Each sensory modality (olfaction, auditory, vision, somatosensory, and taste) can be considered part of the sensory-perceptual attribute component of memory. A language attribute within this framework involves memory representations of phonological, lexical, morphological, syntactical, and semantic information. The attributes within each memory system can be organized in many different ways and are likely to interact extensively with each other, even though it can be demonstrated that these attributes do in many cases operate independent of each other. The organization of these attributes within the event-based memory system can take many forms and are probably organized hierarchically and in parallel. The organization of these attributes within the knowledge-based memory system can take many forms, are assumed to be organized as a set of cognitive maps or neural nets, and their interactions are unique for each memory. It is assumed that long-term representations within cognitive maps are more abstract and less dependent upon specific features. The organization of these attributes within the rule-based memory system can also take many forms; these are assumed to be organized to provide flexibility in executive function in developing rules, development of goals, and affecting decision processes.

Within each system attribute, information is processed in different ways based on different operational characteristics. For the event-based memory system, specific processes involve (a) selective filtering or attenuation of interference of temporary memory representations of new information and is labeled pattern separation, (b) encoding of new information, (c) short-term and intermediate-term memory for new information, (d) the establishment of arbitrary associations, (e) consolidation or elaborative rehearsal of new information, and (f) retrieval of new information based on flexibility, action, and pattern completion.

For the knowledge-based memory system, specific processes include (a) encoding of new information, (b) selective attention and selective filtering associated with permanent memory representations of familiar information, (c) perceptual memory, (d) consolidation and long-term memory storage partly based on arbitrary and/or pattern associations, and (e) retrieval of familiar information based on flexibility and action.

For the rule-based memory system, it is assumed that information is processed through the integration of information from the event-based and knowledge-based memory systems for the use of major processes that include the selection of strategies and rules for maintaining or manipulating information for subsequent decision making and action, as well as short-term or working memory for new and familiar information, development of goals, and affecting decision processes.

On a neurobiological level (see Tables 1, 2, 3) each attribute maps onto a set of neural regions and their interconnected neural circuits. For example, within the event-based memory system, it has been demonstrated that in animals and humans (a) the hippocampus supports memory for spatial, temporal and language attribute information, (b) the caudate mediates memory for response attribute information, (c) the amygdala subserves memory for reward value (affect) attribute information, and (d) the perirhinal and extrastriate visual cortex support memory for visual object attribute information as an example of a sensory-perceptual attribute and the ventral hippocampus supports memory for odor information as another example of a sensory–perceptual attribute (for more detail see Kesner, 1998, 2002).

Within the knowledge-based memory system, it has been demonstrated that in animals and humans (a) the posterior parietal cortex supports memory for spatial attributes, (b) the dorsal and dorsolateral prefrontal cortex and/or anterior cingulate support memory for temporal attributes, (c) the premotor, supplementary motor, and cerebellum in monkeys and humans and precentral cortex and cerebellum in rats support memory for response attributes, (d) the orbital prefrontal cortex supports memory for reward value (affect) attributes, (e) the inferotemporal cortex in monkeys and humans and TE2 cortex in rats subserves memory for sensoryperceptual attributes (e.g. visual objects), and (f) the parietal cortex, Broca and Wernicke’s areas subserve memory for the language attribute (for more detail see Kesner, 1998, 2002). Within the rule-based memory system it can be shown that different subdivisions of the prefrontal cortex support different attributes. For example,( a) the dorso-lateral and ventrolateral prefrontal cortex in humans support spatial, object, and language attributes and the infralimbic and prelimbic cortex in rats supports spatial and visual object attributes, (b) the pre-motor and supplementary motor cortex in monkeys and humans and precentral cortex in rats support response attributes, (c) the dorsal, dorso-lateral, and mid-dorsolateral prefrontal cortex in monkeys and humans and anterior cingulate in rats mediate primarily temporal attributes, and (d) the orbital prefrontal cortex in monkeys and humans and agranular insular cortex in rats support affect attributes (for more detail see Kesner, 2000, 2002).

Given the complexity of the nature of memory representations and the multitude of processes associated with learning and memory associated with any specific task, it is clear that prior to analyzing the neural circuits that support mnemonic processing, one must determine which attributes and which systems and associated underlying processes are essential for memory analysis of the proposed task. One example will suffice. If one assumes that the hippocampus supports the processing of the spatial attribute within the event-based memory system, then any task that minimizes the importance of the spatial attribute and emphasizes the importance of reward value, response, and sensory-perceptual attributes are not likely to involve the hippocampus. I will concentrate in this article primarily on specific processes for which there are sufficient data to determine the role of the neurobiological attribute-based model of memory for the eventbase, knowledge-based, and rule-based components of the attribute model.

Event-Based Memory

For the event-based memory system I will concentrate on specific processes that mediate short-term memory for new information and a selective filtering or attenuation of interference of temporary memory representations of new information which is labeled pattern separation. For the other processes including the establishment of arbitrary associations, consolidation or elaborative rehearsal of new information, and retrieval of new information based on flexibility, action, and pattern completion there is not a sufficient data set to differentiate the contribution of the different attributes associated with mnemonic processing of information.

Short-term or Working Memory — Spatial Attribute

The most extensive data set is based on the use of paradigms that measure the short-term or working memory process such as matching or non-matching-to-sample, delayed conditional discrimination, continuous recognition memory of single or lists of items, and recognition memory based on exploratory information and detection of novelty. Figure 1 depicts the location of the hippocampus in the rat. Figure 2 depicts the location of the different subregions of the hippocampus [dentate gyrus (DG), CA3, and CA1] as well as the medial and lateral perforant path inputs from the entorhinal cortex inputs into the different subregions of the hippocampus in the rat.

With respect to spatial attribute information, there is extensive data that show with the use of the above mentioned paradigms to measure short-term or working memory for spatial information that there are severe impairments for rats, monkeys, and humans with right hippocampal damage or bilateral hippocampal damage (Chiba, Kesner, Matsuo, & Heilbrun, 1990; Hopkins, Kesner, & Goldstein, 1995a; Kesner, 1990; Olton, 1983, 1986; Parkinson, Murray, & Mishkin, 1988; Pigott & Milner, 1993; Smith & Milner, 1981). To examine the temporal dynamic of hippocampal involvement in short-term and intermediate-term memory in the context of processing spatial information in humans, Holdstock, Shaw, and Aggleton (1995) tested patients with hippocampal damage with a delayed matching-to-sample paradigm analogous to tasks used for rats. In this task a single stimulus was presented in a specific location and following delays of 3-40 s, the patients had to remember that location compared with a location not previously seen. The results indicated that there were no memory deficits for delays up to 20 s followed by a deficit at the 40 s delay. However, Cave and Squire (1992) found no deficits for short-term memory for a dot on a line or memory for an angle. In a different experiment, hypoxic subjects with bilateral hippocampal damage were tested on a short-term memory test to determine whether the hippocampus supports short-term or intermediate-term memory for a spatial relationship based on distance information. Control subjects and hypoxic subjects with bilateral hippocampal damage were tested for memory for spatial distance information for delays of 1, 4, 8, 12, or 16 s. The hypoxic subjects had impaired memory for distance information at the long, but not short, delays compared to normal controls (Kesner & Hopkins, 2001).

With respect to specific spatial features, such as allocentric spatial distance, egocentric spatial distance, and spatial location, it has been shown in both rats and humans with bilateral hippocampal damage that there are severe deficits in short-term memory for these spatial features (Long & Kesner, 1996). These data are consistent with the recording of place cells (cells that increase their firing rate when an animal is located in a specific place) within the hippocampus of rats (Kubie & Ranck, 1983; McNaughton, Barnes, & O’Keefe, 1983, O’Keefe, 1983; O’Keefe & Speakman, 1987). One specific example is provided by the use of a continuous spatial recognition memory task where it has been shown that hippocampal lesions produced a profound deficit (Jackson-Smith, Kesner, & Chiba, 1993). However, it should be noted that lesions of the dorsal lateral thalamus, pre- and para-subiculum, medial entorhinal cortex and preand infra-limbic cortex produce profound deficits similar to what has been described for hippocampal lesions, suggesting that other neural regions contribute to the spatial attribute within the event-based memory system (Kesner, et al., 2001). The exact contribution of each of these areas needs to be investigated, especially because grid cells have been recorded from medial entorhinal cortex (Moser et al. 2008) and place cells have been recorded in the parasubiculum (Muller et al., 1996).

Short-term memory for the spatial direction feature has also been investigated. Based on a delayed matching-to-sample task for assessing memory for direction in rats, it was shown that hippocampal lesions disrupt memory for direction (DeCoteau, Hoang, Huff, Stone, & Kesner, 2004). It should be noted that medial caudate nucleus lesions also produced an impairment in memory for direction (DeCoteau et al., 2004). Furthermore, it is likely that spatial short-term memory representations within the hippocampus might be important to amplify a subsequent consolidation process when necessary and spatial short-term memory representations within the pre-and infra-limbic prefrontal cortex might be important to engage a retrieval, action or strategy selection process. Thus, in general, the hippocampus represents some of the spatial features associated with the spatial attribute, within short-term memory.

Based on a subregional analysis of hippocampal function, it appears that different subregions subserve differential roles in spatial processing of short-term memory. For example, using a paradigm developed by Poucet (1989), rats with CA3 or CA1 lesions were tested for the detection of a novel spatial configuration of familiar objects. The results indicated that CA3, but not CA1, lesions disrupted novelty detection of a spatial location (Lee, Jerman, & Kesner, 2005b). Based on the idea that the medial perforant path input into the CA3 or CA1 mediates spatial information via activation of NMDA receptors, rats received direct infusions of AP5 into the CA3 or CA1 and were tested for the detection of a novel spatial configuration of familiar objects and the detection of a novel visual object change using the same paradigm mentioned above. The results indicated that AP5 infusions into the CA3 disrupted both novelty detection of a spatial location and a visual object, whereas AP5 infusions into the CA1 disrupted novelty detection of a spatial location, but not the detection of a novel object (Hunsaker, Mooy, Swift, & Kesner, 2007). In this case, it appears the medial perforant path and the recurrent collateral system in CA3 were either actively maintaining the spatial and non-spatial information as a single behavioral episode in the network over the 3 min intersession interval or else the rich spatial context available to the rats on the test session was sufficient to guide retrieval of the previous experience to guide test performance, reflective of event-based memory processing. CA1, in the absence of recurrent circuitry, appeared to be acting directly upon the spatially rich medial perforant path inputs to retrieve the spatial information needed to perform the test. It is of interest that CA1, as opposed to CA3, did not appear to retrieve the overall behavioral episode in this case to guide retrieval, only the spatial aspects of the experience.

In other research, Lee, Rao, and Knierim (2004) showed physiologically that plasticity mechanisms in CA3 were activated only when animals encountered novel spatial configurations of familiar cues for the first time. Specifically, rats were trained to circle clockwise on a ring track whose surface was composed of four different textural cues (local cues). The ring track was positioned in the center of a curtained area in which various visual landmarks were also available along the curtained walls. To produce a novel cue configuration in the environment, distal landmarks and local cues on the track were rotated in opposite directions (distal landmarks were rotated clockwise and local cues were rotated counterclockwise by equal amounts). It is well known that principal cells in the hippocampus fire when the animal occupies a certain location of space, known as the place field of the cell. Mehta and colleagues (Mehta, Barnes, & McNaughton, 1997; Mehta, Quirk, & Wilson, 2000) originally showed that the location of the CA1 place field (measured by the center of mass of the place field) changed over time (shifting backward opposite to the direction of rat’s motion) in a familiar environment as the animal experienced the environment repeatedly. When the rats encountered the changed cue configurations for the first time in the Lee et al. (2004) experiment, the CA3 place fields shifted their locations backwards prominently compared to the place fields in CA1. However, such a prominent shift was not observed in CA3 from Day 2 onwards (CA1 place fields started to exhibit a similar property from Day 2). This double dissociation in the time course of plasticity between CA1 and CA3 place fields suggests that CA3 reacts rapidly to any changed components in the environment, presumably to incorporate the novel components into an existing event-based shortterm memory system or contribute to a new representation of the environment mediated by an event-based short-term memory system if changes are significant. CA1 appears to be performing a similar function, but within an intermediateterm event-based memory system as demonstrated by the different time course than CA3, suggesting that the representation of the behavioral episode in CA1 is processed on a more lengthy timescale than in CA3. These data suggest that in some cases CA3 processes information and communicates that information to CA1 via the Schaffer collateral projections. This is similar to a finding by Hampson, Heyser, and Deadwyler (1993) who recorded ensembles of CA3 and CA1 neurons during a spatial DNMS task. They found cells responsive to spatial location, nonspatial attributes of the task, as well as cells responsive to conjunctions of spatial and nonspatial information (called conjunctive cells in their report). What is of interest is that quite often they found activity in CA1 to be highly correlated to CA3 activity, but later in time, suggesting information transfer from CA3 to CA1.

Short-term or Working Memory — Temporal Attribute

Memory for duration. In this section I will concentrate on memory for duration rather than the processing of time and time perception as measured by time estimation and time-scale invariance. Previous research has indicated that fimbria-fornix lesioned rats are impaired in remembering the duration of a stimulus across a short delay interval, even though there is only a small change in estimating the passage of time (Meck, Church, & Olton, 1984; Olton, 1986; Olton, Wenk, Church, & Meck, 1988). In an attempt to replicate these results using a different paradigm, rats were trained on a short-term memory for duration task using a delayed symbolic conditional discrimination procedure (Jackson-Smith et al., 1998). It had previously been shown that rats acquire high proficiency in short-term memory for duration information (Santi & Weise, 1995). In the Jackson-Smith et al. (1998) experiment, the rats had to learn that a black rectangle stimulus that was visible for 2 s would result in a positive (go) reinforcement for one object (a ball) and no reinforcement (no go) for a different object (a bottle). However, when the black rectangle stimulus was visible for 8 s then there would be no reinforcement for the ball (no go), but a reinforcement for the bottle (go). After rats learned to respond differentially in terms of latency to approach the object, they received large (dorsal and ventral) lesions of the hippocampus, medial prefrontal cortex (anterior cingulate) lesions, or lesions of the cortex dorsal to the dorsal hippocampus. Following recovery from surgery they were retested. The results indicate that in contrast to cortical control lesions, there were major impairments following hippocampal lesions, as indicated by smaller and statistically non-significant latency differences between positive and negative trials on post-surgery tests. In order to ensure that the deficits observed with hippocampal lesions were not due to a discrimination problem, new rats were trained in an object (black rectangle) duration discrimination task. In this situation the rats were reinforced for either a 2 or 10 s exposure (duration) of the black rectangle. The stimulus was presented and remained visible for either 2 or 10 s, following which the door was raised and latency to move the stimulus was measured. Half of the animals in each group received a piece of Froot Loop on trials with a short stimulus duration and the other half were reinforced on those trials with a long stimulus duration. After rats learned to respond differentially in terms of latency to approach the object, they received large (dorsal and ventral) lesions of the hippocampus, as well as medial prefrontal cortex lesions for comparison purposes, or lesions of cortex dorsal to the dorsal hippocampus. Following recovery from surgery the rats were retested. The results indicate that after hippocampal lesions there was an initial deficit followed by complete recovery.

Thus, the hippocampus mediates memory for duration, but does not mediate duration discrimination. The data are consistent with previous research that indicates that fimbriafornix rats are impaired in remembering the duration of a stimulus across a short delay interval, even though there is only a small change in estimating the passage of time (Meck, Church, & Olton, 1984; Olton, 1986; Olton et al., 1988). Furthermore, it has been suggested that trace conditioning requires memory for the duration of the conditioned stimulus. Thus, it is of importance to note that rabbits with hippocampal lesions are impaired in acquisition (consolidation) of trace but not delayed eye-blink conditioning (Moyer, Deyo, & Disterhoft, 1990). Based on a subregional analysis of hippocampal function, it can be shown that the dorsal CA1 supports object-trace odor paired associate learning (Kesner et al., 2005), but both the dorsal CA1 and CA3 support object trace-place paired associate learning (Hunsaker et al., 2006) and the ventral CA1 supports trace fear conditioning (Rogers et al., 2006). It should be noted that the hippocampus is not directly involved in representing memory information concerning specific objects (Bussey, Saksida, & Murray, 2002; Kesner, Bolland, & Dakis, 1993; Mumby & Pinel, 1994; Norman & Eacott, 2004).

To what extent can one generalize from hippocampal function in rats to humans with respect to memory representation of duration as one feature of temporal attribute information? To answer this question, a number of experiments were conducted using humans that were exposed to hypoxia due to a variety of causes, but primarily carbon monoxide poisoning (Hopkins & Kesner, 1994; Hopkins et al., 1995a). These subjects have anterograde amnesia and, based on MRI data, have bilateral damage to the hippocampus, but no detectable damage to the entorhinal cortex, parahippocampal gyrus, or temporal cortex. They also show no signs of prefrontal cortex dysfunction based on normal performance on tests of fluency and Wisconsin Card sorting. The hypoxic subjects with hippocampus damage and age matched controls were tested for short-term memory for duration of a visual object. Subjects were presented with a single object (square, circle, etc) on a computer screen for a duration of 1 or 3 s. They were instructed to remember the duration of presentation of the object. After a delay of 1, 4, 8, 12, 16, or 20 s, the same object appeared for the same or different duration. The subjects were asked to indicate whether the duration was the same or different from the duration shown in the study phase. The results indicate that the hypoxic subjects were impaired relative to control damaged subjects in short-term memory for duration for all but the shortest delay (Kesner & Hopkins, 2001). In order to determine whether the deficits may have been due to impaired memory for the objects per se, a control task was administered to the same subjects. They were presented with a single object for 1 or 3 s and were asked to remember the object. After a delay of 1, 4, 8, 12, 16, or 20 s either the identical or a different object appeared on the screen. The subjects were asked if it was the same or a different object. The results indicate that there were minimal differences between the hypoxic and control subjects (Kesner & Hopkins, 2001). The impairment could not be due to an inability to estimate time accurately, because in an additional experiment with objects the subjects were asked to estimate the time elapsed before each of the 1, 4, 8, 12, 16, 20 s delay intervals. The results indicate that for hypoxic subjects, time estimates were accurate up to 8 s followed by some underestimation with longer delays, so that short-term memory for the duration of 1 or 3 s stimulus exposure could not be due to difficulty in estimating time (Kesner & Hopkins, 2001). The process of estimating time may not require active participation of short-term memory and may, therefore, appear to be independent of short-term memory for the duration of exposure of a stimulus.

Rat data are also consistent with previous research which indicated that humans with hypoxia resulting in bilateral hippocampal damage are impaired in acquisition (consolidation) of trace but not delayed eye-blink conditioning (Disterhoft, Carrillo, Hopkins, Gabrieli, & Kesner, 1996). Thus, the results suggest that like rodents, humans with hippocampal damage have difficulty in representing short-term memory for duration of an object, but not short-term memory for a single object.

Memory for sequential spatial information. In order to examine sequential learning of spatial information, a task was developed in which rats were required to remember multiple places. During the study phase, rats were presented with four different places within sections that were sequentially visited by opening of one door to a section at a time on a newly devised maze (i.e., Tulum maze). Each place was cued by a unique object that was specifically associated with each location within the section during the study phase. Following a 15 s delay and during the test phase, one door to one section would be opened and in the absence of the cued object in that section, rats were required to recall and revisit the place within that section of the maze that had been previously visited. Once animals were able to reliably perform this short-term episodic memory task, they received lesions to either CA3 or CA1 subregions of the hippocampus. Both CA1 and CA3 lesions disrupted accurate relocation of a previously visited place (Lee et al., 2005a).

In a different task, rats learned trial-unique sequences of spatial locations along a runway box. Each trial consisted of a study phase made up of the presentation of a linear sequence of four spatial locations marked by neutral blocks. After a 30 s interval, the animal was given the test phase. The test phase consisted of the same sequence presented during the study phase, but although one of the spatial locations was not marked by a block, it still contained a reward. The unmarked spatial location was pseudo-randomly distributed equally between the first, second, third, and fourth item in the sequence. To receive a reward, the rat had to visit the correct, unmarked spatial location. Once animals were able to reliably perform this short-term event-based memory task, they received lesions to either CA3 or CA1. Animals with lesions to either CA3 or CA1 had difficulty with short-term event-based memory processing, although CA1 lesioned animals had a much greater deficit. However, when animals were trained on a fixed version of the same task, hippocampal lesions had no effect. These results suggest that CA3 and CA1 both contribute to short-term event-based memory processing, since lesions to CA3 or CA1 result in an inability to process spatial information within the event-based memory system, whereas they have no effect on non-event-based memory information processing (Hunsaker et al., 2008).

In order to determine temporal order memory for visual objects, we used a paradigm described by Hannesson, Howland, and Phillips (2004). This paradigm involves long duration study phases (on the order of minutes) and long duration tests (also on the order of minutes) for temporal preference and thus it is likely to involve more directly the intermediate-term event-based memory processes we have proposed for CA1. In this experiment, rats with CA3 or CA1 lesions were placed inside a box to explore each set of three objects (referred to as A-A, B-B, and C-C) for 5 min with a 3 min inter-session interval. After the third set of objects, the rats were given a 3 min time-out after which one of the two A objects and one of the two C objects were placed in opposite ends of the box. The rats were then returned to the box to measure preference for A versus C for 5 min. On a subsequent day with new objects, the same animals were tested for detection of a novel object as a control using the same procedure previously described with the exception that one of the two A objects and one new object D were placed in opposite ends of the box to measure preference for A versus D for 5 min. All rats were tested once in the A-C preference test (temporal order) and on the A-D preference test (detection of object novelty) for a total of 2 days of testing. The results indicated that CA1 lesions impaired choice (they preferred C over A), but CA3 lesioned rats showed the same preference as controls (they preferred A over C). All groups preferred D in the novelty test (Hoge & Kesner, 2007; Hunsaker et al., 2008). The data indicate that controls prefer A rather than C. In order to explain this preference for A, it is assumed that rats prefer A because the rat has had more time for consolidation within an intermediate-term event-based memory operation for object A in comparison with object C and thus has greater memory strength for object A. Furthermore, CA1, but not CA3, lesioned rats prefer C suggesting an impairment for CA1, but not CA3, in temporal order memory for visual objects. A possible explanation for the observation that CA1 lesioned rats prefer C rather than A is based on the assumption that the trace of A has not been consolidated properly and thus may be difficult to retrieve, but C may still be processed by the short-term event-based memory system mediated by CA3. Thus, the rat prefers C because of a short-term recency effect. This would also explain the lack of deficit observed following a lesion of CA3.

Using the same paradigm as described above but with shorter delays to examine the effects of dorsal and ventral CA1 lesions on temporal and novelty processing of visual objects, odor, and spatial location information revealed that memory for temporal order information for visual objects is impaired following dorsal and ventral CA1 lesions, for odors following ventral CA1, but not dorsal CA1 lesions, and for spatial locations for dorsal CA1, but not ventral CA1 lesions (Hunsaker et al., 2008). Thus, CA1 appears to be involved in separating events in time for spatial and nonspatial information, so that one event can be remembered distinctly from another event, but ventral CA1 might play a more important role than dorsal CA1 for odor information. There were no disruptive effects for dorsal or ventral CA1 lesions on novelty detection for odors, spatial locations, and objects (Hunsaker et al., 2008). It has been shown, however, that lesions to CA3 eliminate any preference for one spatial location over another, suggesting CA3 is also involved in temporal ordering for spatial locations, but only insofar as the information to be temporally processed is spatial in nature.

Short-term or Working Memory — Response Attribute

With respect to response attribute information, it can be shown that with the use of the above mentioned paradigms to measure short-term memory, that for rats with caudateputamen lesions and humans with caudate-putamen damage due to Huntington’s disease (HD), there are profound deficits for a right or left turn response or a list of hand motor movement responses (Cook & Kesner, 1988; Davis, Filoteo, Kesner, & Roberts, 2003; Kesner et al., 1993; Figure 3 depicts the location of the caudate nucleus in the rat). For example, it has been shown that electrolytic induced caudate lesions in rats impair short-term or working memory for a specific motor response (right-left turn) without any impairments in memory for a visual object or for a spatial location (Kesner et al., 1993). Similarly, a lack of effects has been reported following medial caudate lesions in working memory performance for spatial locations on an 8 arm maze (Colombo, Davis, & Volpe, 1989; Cook & Kesner, 1988). A similar pattern of results has been reported following dysfunction of the caudate nucleus in patients with HD. For example, Davis et al. (2003) administered tests of spatial and motor working memory to a small group of HD patients. During the study phase of the spatial memory task, subjects were shown a subset of six stimulus locations (X’s) randomly selected from a set of 16 and presented in a sequential manner. Immediately following the study phase, the test phase was presented. During the test phase, two stimulus locations (X’s) were presented simultaneously. The subject was asked to indicate which one they had seen during the study phase. During the study phase of the hand position memory task, subjects were shown sequential presentations of six hand positions randomly selected from a set of 16 and were asked to imitate the hand position in the display. On the test phase, subjects were shown two pictures of different hand positions and were asked to determine which one they had seen in the study phase. The results of this study indicate that, relative to normal controls, the HD patients are differentially impaired in the motor memory task as compared to the spatial memory task. Interestingly, in two studies, Pasquier, et al. (1994) and Davis, Filoteo and Kesner (2007) demonstrated that HD patients were impaired on a task requiring them to recall the spatial distance of the displacement of a handle on the apparatus. The results of the above mentioned studies, suggest that patients with HD and rats with caudate lesions are impaired on working-memory tasks, particularly when the task places a heavy demand on motor information. Additional data based on a patient with a caudate nucleus lesion showed a decrease in accuracy of memory-guided saccades implying that the caudate nucleus mediates spatial short term memory for eye movements (Vermersch et al., 1999).

Furthermore, rats, monkeys, and humans with caudate lesions have deficits in tasks like delayed response, delayed alternation, and delayed matching to position (Divac, Rosvold, & Szwarcbart, 1967; Dunnett, 1990; Oberg & Divac, 1979; Partiot et al., 1996; Sanberg, Lehmann, & Fibiger, 1978). One salient feature of delayed response, delayed alternation, and delayed matching to position tasks is the maintenance of spatial orientation to the baited food, relative to the position of the subject’s body, often based on proprioceptive and vestibular feedback. These data suggest that the caudateputamen plays an important role in short-term memory representation for the feedback from a motor response feature of response attribute information. The memory impairments following caudate-putamen lesions are specific to the response attribute, because these same lesions in rats do not impair short-term memory performance for spatial location, visual object, or affect attribute information (Kesner et al., 1993; Kesner & Williams, 1995).

Short-term or Working Memory — Affect Attribute

With respect to affect attribute information, it can be shown that with the use of the above mentioned paradigms to measure short-term memory, that for rats with amygdala lesions and humans with amygdala damage there are major deficits for reward value associated with magnitude of reinforcement or for a liking response based on the mere exposure of a novel stimulus (Kesner & Williams, 1995; Chiba, Kesner, Matsuo, & Heilbrun, 1993), suggesting that the amygdala plays an important role in short-term memory representation for reward value as a critical feature of the affect attribute. Figure 1 depicts the location of the amygdala in rats. Since very few studies have measured the role of the amygdala in mediating short-term memory for affect, it was necessary to develop a new task (Kesner & Williams, 1995). In the study phase of the task, rats were given one of two cereals – one cereal contained 25% sugar, the other 50% sugar. One of the two cereals was always designated as the positive stimulus and the other as the negative stimulus. This study phase was followed by the test phase, in which the rat was shown an object which covered a food well. If the rat was given the negative food stimulus during the study phase, no food was placed beneath the object. If the rat was given the positive food stimulus during the study phase, another food reward was placed beneath the object. Latency to approach the object was used as the dependent measure. Rats learn to approach the objects quickly when they expect a reward and they are slow to approach the object when they expect no reward. After they reached criterion of at least a 5 s difference between the positive and negative trials, the rats were given amygdala or control lesions. The results indicate that in contrast to controls, the amygdala lesioned rats displayed a deficit in performance as indicated by smaller latency differences between positive and negative trials on post-surgery tests. This deficit persisted at both short and long delays. In additional experiments, it was shown that the amygdala lesioned rats, like controls, had similar taste preferences and transferred readily to different cereals containing 25% or 50% sugar. A similar result was reported by Kesner, Walser, and Winzenried (1989), who showed that amygdala lesioned rats were impaired in short-term memory performance for 1 versus 7 pieces of food associated with different spatial locations on an 8 arm maze. Thus, the amygdala appears to mediate short-term affect-laden information based on the reward value (magnitude) of reinforcement.

To what extent can one generalize from amygdala function in rats to humans with respect to affect attribute information? Previous research has shown that bilateral damage to the amygdala in humans impairs recognition of affect embedded within facial expressions (Adolphs, Tranel, Damasio, & Damasio, 1994). In order to elaborate further on the role of the amygdala in humans, Chiba et al. (1993) developed a liking test based on the mere exposure effect described by Zajonc (1968). Based on this principle, a computerized liking task was designed to test the presence of the mere exposure effect. The liking task consisted of eight abstract pictures and eight unknown words that were sequentially presented on the computer screen. Following the individual presentation of each of these 16 study stimuli, 16 liking trials were presented. In each liking trial, two stimuli – one study stimulus and a matched lure – were simultaneously presented on the computer screen. Subjects were then asked which of the two stimuli they liked better. Four groups of subjects were tested on this task — college students as control subjects, subjects with partial complex epilepsy of temporal lobe origin, subjects who had undergone unilateral temporal lobe resections, including the temporal cortex and the hippocampus, and subjects who had undergone unilateral temporal lobe resections including the temporal cortex, hippocampus, and amygdala. Results indicated that for mean percent preference for abstract pictures and words, all subject groups showed a stable liking or mere exposure effect for both sets of stimuli, with the exception of those who sustained amygdala damage. It appears that the integrity of the amygdala is critical to the existence of the liking effect.

Thus, it is likely that the amygdala of animals and humans is involved in a short-term memory representation of the affective quality and quantity (reward value) of stimuli. This idea is an extension of earlier theoretical notions that the amygdala is involved in the interpretation and integration of reinforcement (Weiskrantz, 1956), serves as a reinforcement register (Douglas & Pribram, 1966), mediates stimulus-reinforcement associations (Jones & Mishkin, 1972) and serves to associate stimuli with reward value (Gaffan, 1992).

Short-term or Working Memory — Sensory-Perceptual Attribute

With respect to sensory-perceptual attribute information, I will concentrate on visual object information as an exemplar of memory representation of the sensory-perceptual attribute. Figure 4 depicts the location of the perirhinal cortex in the rat. It can be shown that with the use of the above mentioned paradigms to measure short-term memory, that there are severe impairments in visual object information for rats and monkeys with extra-striate or perirhinal cortex lesions (Bussey et al., 2002; Gaffan & Murray, 1992; Horel, Pytko-Joiner, Boytko, & Salsbury, 1987; Kesner et al., 1993; Mumby & Pinel, 1994; Norman & Eacott, 2004; Suzuki, Zola-Morgan, Squire, & Amaral, 1993), suggesting that the extra-striate and perirhinal cortex play an important role in short-term memory representation for visual object information as an exemplar of the sensory-perceptual attribute. Further support derives from single unit studies in rats and monkeys which indicate that activity of neurons in the rhinal cortex reflect stimulus repetition which is an integral part of the delayed non-matching to sample tasks used to measure short-term recognition memory for objects (Zhu, Brown, & Aggleton, 1995).

Using a paradigm developed by Poucet (1989), rats with CA3 lesions that were tested for the detection of a novel visual object change showed no disruption (Lee et al., 2005b). Based on the idea that the lateral perforant path inputs into CA3 mediate visual object information (i.e. “what” information) via activation of opioid receptors, rats received direct infusions of naloxone (a μ opiate antagonist) into CA3 and CA1 and were tested for the detection of a novel spatial configuration of familiar objects and the detection of a novel
visual object. The results indicate that naloxone infusions into the CA3 disrupted novelty detection of a spatial location and a visual object, but naloxone injections into CA1 disrupted novelty detection for a visual object, but not for a spatial location (Hunsaker et al., 2007). The primary implication of these data is that CA3 is capable of simultaneous processing of both spatial (“where”) and nonspatial (“what”) elements of event-based memory. Based on the idea that the medial perforant path inputs into CA3 mediate spatial location information (i.e. “where” information) via activation of NMDA receptors, rats received direct infusions of AP5 (an NMDA antagonist) into CA3 and were tested for the detection of a novel spatial configuration of familiar objects and the detection of a novel visual object. The results indicate that NMDA infusions into the CA3 disrupted novelty detection of a spatial location and a visual object, but NMDA injections into CA1 disrupted novelty detection for a spatial location, but not for a visual object (Hunsaker et al, 2007). Disruption of either medial perforant path (NMDA-ergic) or lateral perforant path (μ opioid-ergic) plasticity resulted in spatial and novel object detection deficits. In CA1, it appears that the spatial and nonspatial elements are processed separately. Disrupting the lateral perforant path by infusing naloxone was sufficient to disrupt novel object detection, but not sufficient to disrupt detection of a spatial change. These data suggest that CA3, but not CA1, is critically important for spatial/nonspatial associative binding critical for eventbased memory. Similar to the argument provided earlier, it appears that CA3 is involved in rapid spatial and nonspatial information binding into coherent behavioral episodes in the time-scale of this task (each episode is of approximately 6 min duration). When CA3 is disrupted, the rat fails to retrieve any elements of the event. This is in contrast to CA1, where it appears that CA1 is involved in temporally tagging information into events, and that this is carried out upon each type of information separately (e.g., spatial and nonspatial information). Thus a disruption to nonspatial information disrupts only nonspatial processing in CA1.

Short term or Working Memory — Language Attribute

With respect to language attribute information, it can be shown that with the use of the above mentioned paradigms to measure short-term memory that there are severe impairments for lists of words for humans with left hippocampal or bilateral hippocampal damage (Hopkins, Kesner, & Goldstein, 1995b), suggesting that the hippocampus plays an important role in short-term memory representation of word information as an important feature of language attribute information. There is a good deal of evidence supporting the idea of important lateralization for hippocampal function in humans with the right hippocampus representing spatial information and the left hippocampus representing linguistic information (Milner, 1971; Smith & Milner, 1981). For example, Milner tested patients who had left or right temporal lobectomies on a task of recall for a visual location. In this task subjects made a mark on an 8 in line in order to reproduce as close as possible the exact position of the previously shown circle. Subjects with right temporal lobe lesions were impaired on this task, whereas subjects with left temporal lobe lesions were not significantly different from control subjects. Smith and Milner (1981) tested patients with right and left temporal lobectomies and control subjects on a memory task involving incidental recall of the locations of the objects. Subjects were asked to estimate the prices of several objects which were placed in a spatial array on a test board. After a short or 24 h delay, subjects were asked to place the objects in their appropriate locations. Left temporal lobe and control subjects performed well on this task at both the immediate and delayed recall of the object locations. Right temporal lobe subjects were impaired for both the immediate and delayed recall of the object locations. Even though hypoxic subjects or left temporal resected patients are impaired for new linguistic information, they are not impaired when they can use semantic or syntactic information to remember the order of presentation of syntactically and semantically meaningful sentences (Hopkins et al., 1995b).

Event-Based Memory — Pattern Separation

Pattern separation is defined as a process to remove redundancy from similar inputs so that events can be separated from each other and interference can be reduced and in addition can produce a more orthogonal, sparse, and categorized set of outputs.

Pattern Separation — Spatial Attribute

The determination of a spatial pattern separation process has been developed extensively by computational models of the subregions of the hippocampus with a special emphasis on the dentate gyrus (DG). Based on the empirical findings that all sensory inputs are processed by the DG subregion of the hippocampus ((Aggleton, Hunt, & Rawlins, 1986; Jackson-Smith et al., 1993; Kesner et al., 1993; Mumby, Wood, & Pinel, 1992; Otto & Eichenbaum, 1992), it has been suggested that a possible role for the hippocampus might be to provide for sensory markers to demarcate a spatial location, so that the hippocampus can more efficiently mediate spatial information. It is thus possible that one of the main process functions of the hippocampus is to encode and separate spatial events from each other. This would ensure that new highly processed sensory information is organized within the hippocampus and enhances the possibility of remembering and temporarily storing one place as separate from another place. It is assumed that this is accomplished via pattern separation of event information, so that spatial events can be separated from each other and spatial interference reduced. This process is akin to the idea that the hippocampus is involved in orthogonalization of sensory input information (Rolls, 1989), in representational differentiation (Myers, Gluck, & Granger, 1995), and indirectly in the utilization of relationships (Cohen & Eichenbaum, 1993).

Rolls’ (1996) model proposes that pattern separation is facilitated by sparse connections in the mossy-fiber system, which connects DG granular cells to CA3 pyramidal neurons. Separation of patterns is accomplished based on the low probability that any two CA3 neurons will receive mossy fiber input synapses from a similar subset of DG cells. Mossy fiber inputs to CA3 from DG are suggested to be essential during learning and may influence which CA3 neurons fire based on the distributed activity within the DG. Cells of the DG are suggested to act as a competitive learning network with Hebb-like modifiability to reduce redundancy and produce sparse, orthogonal outputs. O’Reilly & McClelland (1996) and Shapiro & Olton (1994) also suggested that the mossy fiber connections between the DG and CA3 may support pattern separation.

To examine the contribution of the DG to spatial pattern separation, Gilbert, Kesner, and Lee (2001) tested rats with DG lesions using a paradigm which measured short-term memory for spatial location information as a function of spatial similarity between spatial locations. Specifically, the study was designed to examine the role of the DG subregion in discriminating spatial locations when rats were required to remember a spatial location based on distal environmental cues and to differentiate between the to-be-remembered location and a distractor location with different degrees of similarity or overlap among the distal cues.

Animals were tested using a cheeseboard maze apparatus (the cheese board is similar to a dry land water maze with 177 circular, recessed holes on a 119 cm diameter board) on a delayed-match-to-sample for a spatial location task. Animals were trained to displace an object which was randomly positioned to cover a baited food well in 1 of 15 locations along a row of food wells. Following a short delay, the animals were required to choose between objects which were identical to the sample phase object: one object was in the same location as the sample phase object and the second object was in a different location along the row of food wells. Rats were rewarded for displacing the object in the same spatial location as the sample phase object (correct choice), but they received no reward for displacing the foil object (incorrect choice). Five spatial separations, from 15 cm to 105 cm, were used to separate the correct object and the foil object during the choice phase. Rats with DG lesions were significantly impaired at short spatial separations; however, during the choice phase, performance of DG-lesioned animals increased as a function of greater spatial separation between the correct and foil objects. The performance of rats with DG lesions matched control rats at the largest spatial separation. The graded nature of the impairment and the significant linear improvement in performance as a function of increased separation illustrate a deficit in pattern separation. Based on these results, it was concluded that lesions of the DG decrease the efficiency of spatial pattern separation, which results in impairments on trials with increased spatial proximity and increased spatial similarity among working memory representations. Holden, Hoebel, Loftis, and Gilbert (2012) used an analogous task to that used for rats (Gilbert et al., 2001) to test young participants compared to aged participants who are likely to have DG dysfunction (see Small, et al., 2011). They report that aged participants that do not perform well on standard memory tests are impaired in displaying a pattern separation function. One limitation of the dot task is that it does not assess the ability to separate spatial patterns in the real world. In order to assess real world spatial pattern separation, hypoxic subjects with hippocampal damage and matched normal controls were administered a geographical spatial distance task (cities on a map; Hopkins & Kesner, 1993). The subjects were shown 8 cities on a map of New Brunswick, one at a time, for 5 s each. Subjects were instructed to remember the city and its spatial location on the map. In the test phase, the subjects were presented with the names of two cities that occurred in the study phase and were asked which of the cities was located further to the east (on separate trials, subjects were asked which city occurred further north, south, or west). There were two trials for each compass direction. Spatial distances of 0, 2, 4, and 6 as measured by the number of cities in the study phase that were geographically situated between the two test cities were measured. There were 8 trials for each distance. The hypoxic subjects were impaired for all spatial distances for spatial geographical information compared to control subjects (Hopkins & Kesner, 1993).

Thus, the DG may function to encode and to separate locations in space to produce spatial pattern separation. Such spatial pattern separation ensures that new highly processed sensory information is organized within the hippocampus, which in turn enhances the possibility of encoding and temporarily remembering one spatial location as separate from another.

Based on the observations that cells in CA3 and CA1 regions respond to changes in metric and topological aspects of the environment (Jeffery & Anderson, 2003; O’Keefe & Burgess, 1996), one can ask whether these different features of the spatial environment are processed via the DG and then are subsequently transferred to the CA3 subregion or if these features are communicated via the direct perforant path projection to the CA3 subregion. In both cases, information may then be transferred to the CA1 subregion.

To answer this question, Goodrich-Hunsaker, Hunsaker, and Kesner (2005) examined the contributions of the DG to memory for metric spatial relationships. Using a modified version of an exploratory paradigm developed by Poucet (1989), rats with DG, CA3, and CA1 lesions as well as controls, were tested on tasks involving a metric spatial manipulation. In this task, a rat was allowed to explore two different visual objects separated by a specific distance on a cheeseboard maze. On the initial presentation of the objects, the rat explored each object. However, across subsequent presentations of the objects in the same spatial locations, the rat habituated and eventually spent less time exploring the objects. Once the rat had habituated to the objects in their locations, the metric spatial distance between the objects was manipulated so that the two objects were either closer together or farther apart. The time the rat spent exploring each moved object was recorded. The results showed that rats with DG lesions spent significantly less time exploring the two objects that were displaced relative to controls, indicating that DG lesions impair the detection of metric distance changes. Rats with CA3 or CA1 lesions displayed mild impairments relative to controls, providing empirical validation for the role of DG in spatial pattern separation and support the predictions of computational models (Rolls, 1996; Rolls & Kesner, 2006). Stark, Yassa, and Stark (2010) used an analogous task to that used for rats (Goodrich-Hunsaker et al., 2005) to measure spatial pattern separation based on distance, and in this case angle as well, to test young and healthy aging humans. Even though there were some individual differences, they reporedt an impairment in spatial pattern separation. Also, Baumann, Chan, and Mattingley (2012) reported activation of the posterior hippocampus in spatial pattern separation using the task used by Goodrich-Hunsaker et al. (2005).

Based on the observation that neurogenesis occurs in the DG and that new DG granule cells can be formed over time, it has been proposed that the DG mediates a spatial patternseparation mechanism as well as generates patterns of episodic memories within remote memory (Aimone, Wiles, & Gage, 2006). Thus far, it has been shown in mice that disruption of neurogenesis using low-dose x-irradiation was sufficient to produce a loss of newly born DG cells. Further testing indicated impairments in spatial learning in a delayed non-matching-to-place task in the radial arm maze. Specifically, impairment occurred for arms which were presented with little separation, but no deficit was observed when the arms were presented farther apart, suggesting a spatial pattern separation deficit. Also, the disruption of neurogenesis using lentivirus expression of a dominant Wnt protein produced a loss of newly born DG cells, as well, and was observed in an associative object-in-place task with different spatial separations as a function of the degree of separation, again suggesting a spatial pattern separation deficit (Clelland et al., 2009). These data suggest that neurogenesis in the DG may contribute to the operation of spatial pattern separation. Thus, spatial pattern separation may play an important role in the acquisition of new spatial information and there is a good possibility that the DG may be the subregion responsible for the impairments in the various tasks described above.

Pattern Separation — Temporal Attribute

There are data to support the existence of memory for order information, but it is not always clearly demonstrated whether memory for a particular sequence has been learned and can be accurately recalled. Estes (1986) summarized data demonstrating that, in human memory, there are fewer errors for distinguishing items (by specifying the order in which they occurred) that are far apart in a sequence than those that are temporally adjacent. Other studies have also shown that order judgments improve as the number of items in a sequence between the test items increases (Banks, 1978; Chiba, Kesner, & Reynolds, 1994; Madsen & Kesner, 1995). This phenomenon is referred to as a temporal distance effect [sometimes referred to as a temporal pattern separation effect (Kesner, Lee, & Gilbert, 2004)]. The temporal distance effect is assumed to occur because there is more interference for temporally proximal events than for temporally distant events.

Based on these findings, Gilbert et al. (2001) tested rodents memory for the temporal order of items in a one-trial sequence learning paradigm. In the task, each rat was given one daily trial consisting of a sample phase followed by a choice phase. During the sample phase, the animal visited each arm of an 8-arm radial maze once in a randomly predetermined order and was given a reward at the end of each arm. The choice phase began immediately following the presentation of the final arm in the sequence. In the choice phase, two arms were opened simultaneously and the animal was allowed to choose between the arms. To obtain a food reward, the animal had to enter the arm that occurred earlier in the sequence that it had just followed. Temporal separations of 0, 2, 4, and 6 were randomly selected for each choice phase. These values represented the number of arms in the sample phase that intervened between the arms that were to be used in the test phase. After reaching criterion, rats received CA1 lesions. Following surgery, control rats matched their preoperative performance over all temporal separations. In contrast, rats with CA1 lesions performed at chance across 0, 2, or 4 temporal separations and a little better than chance in the case of a separation of 6 items. The results suggest that the CA1 subregion is involved in memory for spatial location as a function of temporal separation of spatial locations; lesions of the CA1 decrease efficiency in temporal pattern separation. CA1 lesioned rats cannot separate events over time, perhaps due to an inability to inhibit interference that may be associated with sequentially occurring events. The increase in temporal interference impairs the rat’s ability to remember the order of specific events. Tolentino et al. (2012) used an analogous task to that used for rats (Gilbert et al., 2001) to test young compared to non-demented older participants in a spatial temporal pattern separation task and report temporal pattern separation problems for the older participants. In another spatial location task, patients with a hypoxic condition and hippocampal damage were impaired in displaying a temporal pattern separation function (Hopkins et al., 1995a).

In a more recent experiment using a paradigm described by Hannesson et al. (2004), it was shown that temporal order information for spatial location was impaired only for CA1 (Hunsaker et al., 2008). Thus, it can be suggested that the CA1 hippocampal subregion serves as a critical substrate for sequence learning and temporal pattern separation for the spatial attribute.

It has been suggested that the perirhinal cortex and CA1 subregion of the hippocampus plays an important role in supporting temporal processing of visual object information (Hoge & Kesner, 2007; Hunsaker et al., 2008). In humans it can be shown that a temporal pattern separation process can be observed in hypoxic patients in a temporal order test memory test for abstract figures (Hopkins et al., 1995a), suggesting that the hippocampus may also play a role in temporal pattern separation for visual stimuli, at least in humans.

Does the hippocampus support temporal pattern separation processes for sensory-perceptual information other than space and visual objects? To answer this question, memory for the temporal order for a sequence of odors was assessed in rats based on a varied sequence of five odors, using a similar paradigm described for sequences of spatial locations. Kesner, Gilbert, and Barua (2002) found that rats with hippocampal lesions were impaired relative to control animals for memory for all temporal distances between the odors, despite an intact ability to discriminate between the odors. Fortin, Agster, and Eichenbaum (2002) reported similar results with fimbria fornix lesions. In a further subregional analysis, rats with dorsal CA1 lesions showed a mild impairment in memory for the temporal distance for odors, but rats with ventral CA1 lesions showed a severe impairment (Kesner, Hunsaker, & Ziegler, 2010). Thus, the CA1 appears to be involved in separating events in time for spatial and nonspatial information, so one event can be remembered distinctly from another event; however, the dorsal CA1 might play a more important role than the ventral CA1 for spatial information (Chiba, Johnson, & Kesner, 1992), and conversely the ventral CA1 might play a more important role than the dorsal CA1 for odor information. The mechanism that could subserve the above mentioned findings is based on the memory question that asks which of two items occurred earlier in the list. To implement this type of memory, some temporally decaying memory trace or temporally increasing memory trace via a consolidation process might provide a model (Marshuetz, 2005); in such a model, temporally adjacent items would have memory traces of more similar strength and would be harder to discriminate than the strengths of the memory traces of more temporally distant items.

Pattern Separation — Response Attribute

A delayed-match-to-sample task was used to assess memory for motor responses in rats with control, hippocampus, or medial caudate nucleus (MCN) lesions. All testing was conducted on a cheeseboard maze in complete darkness using an infrared camera. A start box was positioned in the center of the maze facing a randomly determined direction on each trial. In the sample phase, a phosphorescent object was randomly positioned to cover a baited food well in 1 of 5 equally spaced positions around the circumference of the maze forming a 180-degree arc 60 cm from the box. On each trial, the door to the start box was opened, the rat exited, displaced the object to receive food, and returned to the box. The box was then rotated to face a different direction. The food well in the same position relative to the box was baited and an identical phosphorescent object was positioned to cover the well. A second identical object was positioned to cover a different unbaited well. On the choice phase, the rat was allowed to choose between the 2 objects. The object in the same position relative to the start box as the object in the sample phase was the correct choice and the foil object was the incorrect choice. The rat must remember the motor response made on the sample phase and make the same motor response on the choice phase to receive a reward. Four separations of 45, 90, 135, and 180 degrees were randomly used to separate the correct object from the foil in the choice phase. Hippocampus-lesioned and control rats improved as a function of increased angle separation and matched the performance of controls. However, rats with MCN lesions were impaired across all separations (Kesner & Gilbert, 2006). Results suggest that the MCN, but not the hippocampus, may support working memory and/or a process aimed at reducing interference for motor response selection based on vector angle information.

Pattern Separation — Affect Attribute

Male Long-Evans rats were tested on a modified version of Flaherty, Turovsky, & Krauss’ (1994) anticipatory contrast paradigm, to assess pattern separation for reward value. Prior to testing, each rat received either a control, hippocampal, or amygdala lesion. In the home cage, each rat was allowed to drink a water solution containing 2% sucrose for 3 min followed by a water solution containing 32% sucrose for 3 min. Over 10 days of testing, the rats in each lesion group showed significantly increased anticipatory discriminability as a function of days. In order to assess the operation of a pattern separation mechanism, each rat was then tested using the same procedure, except the 2% solution was followed by a 16% solution for 10 days and then by an 8% solution for 10 days. Control and hippocampal-lesioned rats continued to show high discriminability when the 2% solution was followed by a 16% solution, however, the amygdala-lesioned rats showed low anticipatory discriminability. On trials where the 2% sucrose solution was followed by an 8% sucrose solution, all groups showed low discriminability scores, suggesting that when two reward values are very similar, even control animals are not able to separate the reward values in memory. However, the results of a preference task revealed that all groups can perceptually discriminate between a 2% and an 8% sucrose solution (Gilbert & Kesner, 2002). The data suggest that the amygdala, but not the hippocampus, is involved in the separation of patterns based on reward value.

Pattern Separation — Sensory-Perceptual Attribute (Objects)

In order to determine whether the perirhinal cortex plays a role in object-based pattern separation, rats with perirhinal cortex, hippocampal, or sham lesions were trained on a successive discrimination go/no-go task to examine recognition memory based on pattern separation for an array of visual objects with varying interference among the objects in the array. Rats were trained to recognize a target array consisting of four particular objects that could be presented in any one of four possible configurations to cover baited food wells. If the four target objects were presented, the rat should displace each object to receive food. However, if a novel object replaced any one or more of the target objects, then the rat should withhold its response. The number of novel objects presented on non-rewarded trials varied from one to four. The fewer the number of novel objects in the array, the more interference the array shared with the target array, therefore increasing task difficulty, requiring an object pattern separation mechanism to solve the task. The results indicated that an increased number of novel objects resulted in a pattern separation effect with less interference for the target array as indicated by decreased task difficulty. Although accuracy was slightly lower in rats with hippocampal lesions, compared to controls, the learning of the groups was not statistically different. In contrast, rats with perirhinal cortex lesions were significantly impaired in utilizing a pattern separation function compared to both control and hippocampal-lesioned rats (Gilbert & Kesner, 2003). The results suggest that temporal pattern separation for objects is affected by stimulus interference in rodents and is mediated by the perirhinal cortex. Other research supports these results for the perirhinal mediation of object-based pattern separation (Bussey et al., 2002; Norman & Eacott, 2004).

In studies with humans, a modified continuous recognition task was used. In one study with young participants using high resolution fMRI with this task, it was found that the hippocampus distinguished among correctly identified true stimulus repetitions, correctly rejected presentations of similar lure stimuli, and false alarm lures (Kirwan & Stark, 2007). In a subsequent study it was shown that in aged compared to young participants that the DG/CA3 subregions of the hippocampus played an important role in deficits found in aged participants (Yassa et al., 2010). For a review of the human pattern separation data see (Yassa & Stark, 2011).

Pattern Separation — Sensory-Perceptual Attribute (Odors)

Working memory and pattern separation for odor information was assessed in rats using a matching-to-sample for odors paradigm. The odor set consisted of a five aliphatic acids with unbranched carbon chains that varied from two-six carbons in length. Each trial consisted of a sample phase followed by a choice phase. During the sample phase, rats would receive one of five different odors. During the choice phase 15 s later, one of the previous odors was presented simultaneously side by side with a different odor that was based on the number of aliphatic acids that varied in the carbon chains from two-six carbons in length and rats were allowed to choose between the two odors. The rule to be learned in order to receive a food reward was to always choose the odor that occurred during the study phase. Odor separations of 1, 2, 3 or 4 were selected for each choice phase which represented the carbon chain difference between the study phase odor and the test phase odor. Once an animal reached a criterion of 80-90% correct across all temporal separations based on the last 16 trials, rats received a control or ventral dentate gyrus lesion and were retested on the task. On postoperative trials, there were no deficits at the 15 s delay for either the controls or the ventral dentateyrus lesioned rats. However, when the delay was increased o 60 s, rats with ventral DG lesions were significantly impaired at short spatial separations and performance of DGlesioned animals increased as a function of greater spatial separation between the correct and foil objects. The performance of rats with ventral DG lesions matched control rats at the largest odor based separation. The graded nature of the impairment and the significant linear improvement in performance as a function of increased separation illustrate a deficit in odor pattern separation. Based on these results, it was concluded that lesions of the ventral DG decrease the efficiency of odor based pattern separation, which results in impairments on trials with increased spatial proximity and increased odor similarity among working memory representations (Weeden, Hu, Ho, & Kesner, 2012). The data suggest that the ventral hippocampus, but not dorsal hippocampus, supports pattern separation for odor information.

In summary, within the event-based memory system, different brain regions process different attributes in support of short-term or working memory and pattern separation processes. Data are presented to support this assertion by demonstrating that the dorsal hippocampus mediates spatial and temporal attribute information, the caudate mediates response attribute information, the amygdala mediates affect attribute information, the perirhinal cortex mediates sensory-perceptual attribute information for visual objects, the ventral hippocampus mediates sensory-perceptual attribute information for odors, and the hippocampus mediates language attribute information. Where data are available, there are parallel results found in rodents, monkeys and humans.

Knowledge-Based Memory

The organization of the attributes within the knowledge-based memory system can take many forms and they are assumed to be organized as a set of cognitive maps or neural nets, the interactions of which are unique for each memory. It is assumed that long-term representations within cognitive maps are more abstract and less dependent upon specific features. Some interactions between attributes are important and can aid in identifying specific neural regions that might subserve a critical interaction. For example, the interaction between sensory-perceptual attributes and the spatial attribute can provide for the long-term memory representation of a spatial cognitive map or spatial schemas, the interaction between temporal and spatial attributes can provide for the long-term memory representation of scripts, the interaction between temporal and affect attributes can provide for the long-term memory representation of moods, and the interaction between sensory-perceptual and response attributes can provide for the long-term memory of skills. Based on a series of experiments, it can be shown that within the knowledge-based memory system, different neural structures and circuits mediate different forms or attributes of memory. The most extensive data set is based on the use of paradigms that measure repetition priming, the acquisition of new information, discrimination performance, executive functions, strategies and rules to perform in a variety of tasks including skills and the operation of a variety of long-term memory programs.

For the knowledge-based memory system, I will concentrate on specific processes that mediate perceptual memory within long-term memory. For the other processes, including selective attention and selective filtering associated with permanent memory representations of familiar information, selection of strategies and rules (“executive functions”), and retrieval of familiar information based on flexibility and action, the establishment of arbitrary associations, consolidation or elaborative rehearsal of new information, and retrieval of new information based on flexibility, action, and pattern completion, there is not a sufficient data set to differentiate the contribution of the different attributes associated with mnemonic processing of information.

Spatial Attribute

The emphasis will be on the role of the parietal cortex (PPC) in perceptual and long-term memory processing of complex spatial information within the knowledge-based memory system, see Figure 4 for the location of the PPC in rats. Rats with PPC lesions display deficits in both the acquisition and retention of spatial navigation tasks that are presumed to measure the operation of a spatial cognitive map within a complex environment (DiMattia & Kesner, 1988b; Kesner, Farnsworth, & Kametani, 1992). They also display deficits in the acquisition and retention of spatial recognition memory for a list of five spatial locations (DiMattia & Kesner, 1988a). In a complex discrimination task in which a rat has to detect the change in location of an object in a scene, rats with PPC lesions are profoundly impaired (DeCoteau & Kesner, 1996), yet on less complex tasks involving the discrimination or short-term memory for single spatial features including spatial location, allocentric and egocentric spatial distance (Long & Kesner, 1996) there are no impairments. When the task is more complex, involving the association of objects and places (components of a spatial cognitive map), then PPC plays an important role. Support for this conclusion comes from the finding that rats with PPC lesions are impaired in the acquisition and retention of a spatial location plus object discrimination (paired associate task), but show no deficits for only spatial or object discriminations (Long et al., 1998). Comparable deficits are found within an egocentric-allocentric distance paired associate task (Long & Kesner, 1996), but there is no deficit for an object-object paired associate task, suggesting that spatial features are essential in activating and involving the PPC (unpublished observations). Finally, it should be noted that in rats, neurons have been found within the PPC that encode spatial location and head direction information and that many of these cells are sensitive to multiple cues including visual, proprioceptive, sensorimotor and vestibular cue information (Chen, Lin, Barnes, & McNaughton, 1994; McNaughton, Chen, & Marcus, 1991). Additional support comes from studies with parietal lesioned monkeys. These animals demonstrate deficits in place reversal, landmark reversal, distance discrimination, bent wire route-finding, pattern string-finding, and maze-learning tasks (Milner, Ockleford, & DeWar, 1977; Petrides & Iversen, 1979; Pohl, 1973).

In a somewhat different study, rats with PPC lesions are impaired in an implicit spatial repetition priming experiment but perform without difficulty in processing positive priming for features of visual objects and a short-term or working memory for a spatial location experiment (Chiba, Kesner, & Jackson, 2002), suggesting that the parietal cortex plays a role in spatial perceptual memory within the knowledge-based memory system, but does not play a role in spatial memory within the event-based memory system.In humans there is a general loss of topographic sense, which may involve loss of long-term geographical knowledge as well as an inability to form cognitive maps of new environments. Using PET scan and functional MRI data, it can be shown that complex spatial information results in activation of the parietal cortex (Ungerleider, 1995). Thus, memory for complex spatial information appears to be impaired (Benton, 1969; De Renzi, 1982). Furthermore, in patients with parietal lesions and spatial neglect, there is a deficit in spatial repetition priming without a loss in short-term or working memory for spatial information (Ellis, Sala, & Logie, 1996). Keane, et al. (1995) reported that a patient with occipital-lobe damage (extending into PC) showed a deficit in perceptual priming but had no effect on recognition memory, whereas a patient with bilateral medial temporal lobe damage (including hippocampus) had a loss of recognition memory, but no loss of perceptual memory.

Sensory-Perceptual Attribute

The emphasis will be on visual perceptual processing within the knowledge-based system, see Figure 4 for the location of the TE2 cortex in the rat. I will concentrate on temporal cortex (TE2) and make comparisons with the (PPC). In rats using a visual object-place recognition task, TE2 lesioned rats fail to detect a visual object change, whereas PPC lesioned rats fail to detect a spatial location change (Tees, 1999) suggesting that the two cortical areas play a distinctive role in perceptual processing of visual versus spatial location information. Similar results were reported by Ho et al. (2011) who showed that rats with TE2 lesions had object recognition problems at 20 min, but not at 5 min delays. In rats there is a deficit in processing positive priming for features of visual objects (a component of perceptual memory system), but the rats performed well in positive priming for spatial location (Kesner, unpublished observations). In monkeys deficits for visual objects and in a working memory task and visual paired comparison task were observed following TE2 deficits suggesting that TE2 cortex may play an important role in visual perceptual processing (Buffalo et al., 1999). It can also be shown that lesions of the inferotemporal cortex in monkeys and humans and temporal cortex (TE2) in rats result in visual object discrimination problems (Dean, 1990; Fuster,1995; Gross, 1973; McCarthy & Warrington, 1990; Weiskrantz & Saunders, 1984), suggesting that the inferotemporal or TE2 may play an important role in mediating long-term representations of visual object information. Additional support comes from PET scan and functional MRI data in humans, where it can be shown that visual object information results in activation of inferotemporal cortex (Ungerleider, 1995). In a somewhat different study, Sakai and Miyashita (1991) have shown that neurons within the inferotemporal cortex responded more readily after training to a complex visual stimulus that had been paired with another complex visual stimulus across a delay, suggesting the formation of long-term representations of objectobject pairs.

In summary, within the knowledge-based memory system, different brain regions process different attributes in support of perceptual processes. Data are presented to support this assertion by demonstrating that the PPC mediates the spatial attribute for spatial perceptual information and the TE2 cortex mediates the sensory-perceptual attribute for visual object information. Where data are available, there are parallel results found in rodents, monkeys and humans.

Rule-Based Memory

For the rule-based memory system, it is assumed that there is integration of information from the event-based and knowledge-based memory systems for the use of major processes that include the selection of strategies and rules for maintaining or manipulating information for subsequent decision making and action as well as short-term or working memory for new and familiar information.

I will concentrate on two processes, namely working (short-term memory) and paired associate learning and I will emphasize the importance of prefrontal cortex (PFC) subregions in the mediation of different attributes. Figure 5 depicts the organization of the PFC in the rat.

Working Memory (Short-term Memory)

Working or short-term memory is a process for short-term active maintenance of information as well as for processing maintained information. The most extensive data set aimed at addressing the role of the different PFC subregions in supporting different forms of working memory is based on experiments using paradigms that measure short-term or working memory in tasks such as matching or non-matching-to-sample for single or lists of items, continuous or nback recognition memory, and novelty detection based on recognition memory.

Evidence supporting a role for the rat PFC in working memory is based on the findings that lesions of the anterior cingulate and precentral (AC/PC) cortex that spare the prelimbic-infralimbic (PL/IL) cortex produce a deficit in working memory for motor response information such as working memory for a motor (right-left turn) response (Kesner, Hunt, Williams, & Long, 1996; Ragozzino & Kesner, 2001), but not working memory for visual object (Ennaceur, Neave, & Aggleton, 1997; Kesner et al., 1996; Shaw & Aggleton, 1993), or affect (food reward value) information, (DeCoteau, Kesner, & Williams, 1997; Ragozzino & Kesner, 1999). There are also no deficits, with a few exceptions, in working memory for spatial information using delayed non-matching to position, delayed spatial alternation, or non-matching-to sample in a T- maze, 8 arm maze, and continuous spatial recognition memory procedures (Ennaceur et al., 1997; Harrison & Mair, 1996; Kesner et al., 1996; Kolb, Sutherland, & Whishaw, 1983; Passingham, Myers, Rawlins, Lightfoot, & Fearn, 1988; Ragozzino, Adams, & Kesner, 1998; Sanchez-Santed, deBruin, Heinsbroek, & Verwer, 1997; Shaw & Aggleton, 1993). Thus, the data suggest that the AC cortex and PC cortex process working memory for motor response information, but do not process working memory for visual object, spatial, or affect (food value) information. In monkeys, enhanced single unit activity was recorded in the premotor cortex in relation to the go and no-go component of a delayed conditional go/no go task, suggesting an involvement of the premotor cortex in a working memory task component associated with motor movement (Watanabe, 1986). Recent work with humans using fMRI techniques showed that activation was observed in the premotor cortex in a delayed response task (Turner & Levine, 2006).The PL-IL cortex appears to play an important role in working memory for visual object and spatial location information. Supporting evidence is based on the findings that lesions of the PL-IL cortex produce deficits in working memory for spatial information (Brito & Brito, 1990; Delatour & Gisquet-Verrier, 1996; Granon, Vidal, Thinus-Blanc, Changeux, & Poucet, 1994; Horst & Laubach, 2009; Ragozzino et al., 1998; Seamans, Floresco, & Phillips, 1995), and working memory for visual object information (Di Pietro, Black, Green-Jordan, Eichenbaum, & Kantak, 2004; Kesner et al., 1996; Ragozzino, Detrick, & Kesner, 2001). However, PL-IL lesions do not produce a deficit in working memory for a food reward (DeCoteau et al., 1997; Ragozzino & Kesner, 1999). Further support of this conclusion was reported by Chang, Chen, Luo, Shi, and Woodward (2002), who found sustained neural firing in the PL-IL cortex during the delay within a delayed matching-to-position task, and Baeg et al. (2003) who recorded from the PL-IL cortex in a spatial delayed alternation task reported an increase in neural firing during the delay period.

In early research, Goldman-Rakic (1987, 1996) proposed that one could fractionate functions of the PFC on the basis of differential subregional contributions. She suggested that the main function of the PFC is to support working memory defined as a specialized process by which a remembered stimulus is held on line to guide behavior in the absence of external cues. Furthermore, she postulated a modular organization of working memory based on the use of different domains or attributes of information processing. This is called the domain-specificity model, which would be consistent with the attribute model. In monkeys, the cortex surrounding the principal sulcus (dorsolateral prefrontal cortex) is specialized for on line processing of spatial information, whereas the inferior convexity (ventrolateral prefrontal cortex) is specialized for on-line processing of visual object information. In addition, the ventrolateral prefrontal cortex could also support other sensory domains. Support for this model is based on the observation of delay-specific cells in the dorsolateral prefrontal cortex for only spatial tasks, such as delayed response, delayed alternation, and delayed oculomotor tasks, and the observation that lesions of the dorsolateral prefrontal cortex disrupt performance on a delayed response, delayed alternation, delayed oculomotor, and spatial search tasks (Butters & Pandya, 1969; Funahashi, Bruce, & Goldman-Rakic, 1993; Goldman & Rosvold, 1970; Mishkin, 1957; Passingham, 1985). In contrast, delay-specific cells for a visual object delay task are found in the ventrolateral prefrontal cortex and lesions in this area disrupt visual object recognition (Mishkin & Manning, 1978; Wilson, Scalaidhe, & Goldman-Rakic, 1993).

Challenges to Goldman-Rakic’s domain specificity model for working memory as the main organizing principle for the prefrontal cortex have emerged based on research with monkeys and humans. First, Rao, Rainer, and Miller (1997) have shown that one can record both spatial location and visual object information from the same cell within the dorsolateral prefrontal cortex and these cells can change very readily based on the demands of the task. Second, Fuster, Bauer, and Jervey (1982) showed that delay cells can be found in both the dorsolateral and ventrolateral prefrontal cortex in a visual-visual stimulus or spatial location-spatial location matching-to-sample tasks. In humans, D’Esposito et al. (1998) reported a meta-analysis of neuroimaging results based on visual object and spatial location working memory tasks which provided a strong case for processing of both visual object and spatial location information in working memory in both the dorsolateral and ventrolateral prefrontal cortex. Similar findings based on a meta-analysis of sixty PET and fMRI studies using working memory paradigms were reported by Wager and Smith (2003), showing that working memory for spatial and object location information resulted in activation of the ventral and dorsolateral prefrontal cortex. Using another meta-analytic review, Owen (2000) reported results that support the findings mentioned in the above studies. Thus, it is clear that there is a large body of evidence based on recording and lesion studies supporting a working memory or short-term memory role for the PL-IL cortex in rats and the ventral and dorsolateral frontal cortex in monkeys and humans in working memory for spatial locations and objects.

Based on anatomical and behavioral data, the agranular insular and lateral orbital (AI/LO) cortex appears to play an important role in working memory for affect attribute information based on odor and taste. Supporting evidence is based on the findings that lesions of the AI/LO cortex produce deficits in working memory for affect based on taste or odor information (DeCoteau et al., 1997; Di Pietro et al., 2004; Otto & Eichenbaum, 1992; Ragozzino & Kesner, 1999). Further support can be found in a study where sustained neuronal firing was observed in the AI/LO cortex during the delay period in a non-matching-to-sample for odors task (Ramus & Eichenbaum, 2000). It should be noted that AI/LO cortical lesions do not produce an effect on visual object or spatial working memory (DeCoteau et al., 1997; Di Pietro et al., 2004; Horst & Laubach, 2009; Ragozzino & Kesner, 1999), although in a recent study there were deficits using a delayed alternation task for odors with lesions of the PL-IL cortex, but there was extra damage to the AI/LO cortex (Kinoshita et al., 2008). Deficits in short-term memory for odors have been reported following damage to the orbital frontal cortex in humans (Jones-Gotman & Zatorre, 1993). Furthermore, Dade et al. (2001) reported increased activity based on PET scans in the orbital frontal cortex as well as dorsal and ventral lateral prefrontal cortex in an n-back task based on odor information.

In summary, within the rule-based memory system, different brain regions process different attributes in support of short-term or working memory. Data are presented to support this assertion by demonstrating that the AC/PC cortex mediates response attribute information, the PL/IL mediates spatial location and visual object attribute information, and AI/OL cortex mediates odor and taste information within the sensory-perceptual attribute. It appears that the different regions can be dissociated from each other based on specific attributes. Parallel results are found in monkeys and humans in that response information is mediated by the premotor cortex, spatial location and visual object information are mediated by the dorsal and ventral lateral prefrontal cortex, and odor and taste information are mediated by the orbital frontal cortex.

Paired Associate Learning

It is assumed that in addition to processing of temporal information, the prefrontal cortex is also involved in mediating higher-order processes, such as rule learning based on the use of biconditional discrimination or paired associate paradigms. Passingham et al. (1988) showed that lesions of the AC and PC cortex in rats resulted in deficits in a visual conditional motor associative task. Similar results with the same type of lesion and the same visual conditional motor associative task were reported by (St-Laurent, Petrides, & Sziklas, 2009). Based on behavioral and anatomical data, the PC cortex in the rat is assumed to be homologous to the premotor and supplementary motor area in monkeys and humans (periarcuate or posterior dorsal lateral area; Brodmann areas 6 and 8) in that the deficits observed in the visual-response conditional task and working memory for a motor response task in rats are rather similar to what has been described for monkeys and humans. For example, in monkeys, Halsband and Passingham (1985) showed that the premotor cortex is directly involved in mediating a visual-conditional motor task, but not a visual conditional non-motor task, suggesting that the response component is critical. Similar deficits in a visual conditional response task following premotor cortex lesions have been found in monkeys and humans (Halsband & Freund, 1990; Petrides, 1982, 1985a, 1997). Based on fMRI analysis of learning arbitrary visual-response associations, increased activity in the dorsal premotor cortex (Toni, Ramnani, Josephs, Ashburner, & Passingham, 2001) has been observed in addition to supplementary motor cortex interacting with dorsolateral prefrontal cortex (Boettiger & D’Esposito, 2005).

In a different set of studies, it has been shown that rats with lesions of the PL/IL, but not of the AC and PC cortex, fail to acquire an object-place association (Kesner & Ragozzino, 2003). In a subsequent study Lee and Solivan (2008) showed that temporary inactivation of the PL/IL cortex with muscimol led to profound impairments in an object-place paired association task. Furthermore, impairment in a novelty detection paradigm using an object-in-place learning task has been observed in in rats with PL/IL lesions (Barker, Bird, Alexander, & Warburton, 2007).

Based on a different set of arbitrary associations, it has been shown in rats that lesions of AI/LO impair the learning of a cross-modal association involving odor and tactile stimuli (Whishaw, Tomie, & Kolb, 1992). Furthermore, based on single unit recording within the OL cortex of rats, it was found that many neurons were active during odor-location learning. In monkeys, in the orbital frontal cortex, many neurons were activated by both taste and odor stimuli (Rolls & Baylis, 1994), suggesting that flavor may be processed by orbital frontal cortex leading to pleasant experiences often associated with reward. In humans, Small et al. (1999) showed that based on fMRI data there was activation of the orbital frontal cortex during the processing of taste and odor information.

In the context of other types of paired associate learning, Petrides (1985b) has shown that humans with PFC cortex lesions have difficulty in learning a paired associate task and Pigott and Milner (1993) reported that frontal lobe damaged patients are impaired for objects and places in a complex visual scene task. Also, Klingberg and Roland (1998) showed that based on PET scans, the dorsal lateral prefrontal and anterior cingulate cortex are activated during new learning of visual-auditory paired associates.

There are many interactions among the three systems. I will present just one example, namely an interaction between the prefrontal cortex (rule-based system) and hippocampus (event-based memory system) in the context of temporal processing of short-term memory information. In this study Lee and Kesner (2003) examined the dynamic interactions between the prefrontal cortex and hippocampus by training and testing rats on a delayed non-matching-to-place task on a radial 8-arm maze requiring memory for a single spatial location following short-term (i.e., 10 s or 5 min) delays. The results showed that inactivating both regions at the same time resulted in a severe impairment of short-term and intermediate memory for spatial information, suggesting that one of the structures needs to function properly for intact processing of short-term or intermediate-term spatial memory. Thus, the two regions interact with each other to ensure the processing of spatial information oveer a dynamic temporal range including both short-term and intermediate term memory. The current results provide compelling evidence indicating that a mnemonic time window is a critical factor in dissociating the function of the hippocampal system from that of the medial prefrontal cortex in a delayed choice task. That is, the dorsal hippocampus and medial prefrontal cortex appear to process spatial memory in parallel within a shortterm range, whereas the dorsal hippocampal function becomes more essential once the critical time window requires spatial memory for a time period exceeding that range.

In summary, within the rule-based memory system different brain regions process different attributes in support of paired associate learning. Data are presented to support this assertion by demonstrating that the PC cortex mediates associative processes based on a visual conditional response learning, the PL/IL cortex mediates associative processes based on visual-spatial learning, and AI/LO cortex mediates associative processes based primarily on odor-taste learning. PPC mediates the spatial attribute for spatial perceptual information and the TE2 cortex mediates the sensory-perceptual attribute for visual object information. Parallel results are found in monkeys and humans in that for monkeys and humans the premotor mediates associative processes based on visual conditional response learning, the dorsal and ventral lateral prefrontal cortex mediate object-place learning, and the orbital prefrontal cortex mediates odor-taste associations.

Other Theories of Memory

I will compare the tripartite attribute model with the O’Keefe and Nadel (1978) and Moscovitch et al. (2005); the Squire (1994) and Squire et al. (2004); and the Cohen and Eichenbaum, (1993), Eichenbaum (2004) and Eichenbaum et al., (2007) models. I will apply Schacter and Tulving (1994) suggestion that one needs to define memory systems in terms of the kind of information to be represented, the processes associated with the operation of each system, and the neurobiological substrates including neural structures and mechanisms that subserve each system.

O’Keefe and Nadel (1978) have concentrated on the role of the hippocampus as the neurobiological substrate in processing spatial and contextual information and more recently Nadel and Moscovitch (1998) have suggested that the hippocampus stores episodic memory. They do suggest that transfer of episodic information to the neocortex can occur to store semantic memories. The tripartite attribute model does not accept that all episodic (event-based) memories are stored in the hippocampus and emphasizes that the hippocampus supports temporal, odor, and language information in addition to space and context. The tripartite attribute model also states that new information is stored in a semantic or knowledge-based memory system via a consolidation process, but the model emphasizes that different neocortical areas store different attributes of memory which utilizes different processes from the event-based memory system, such as perception, selective attention, and implicit memory. The Nadel model does not incorporate the prefrontal cortex in their memory model.

Squire proposes a declarative versus non-declarative system long-term memory model (Squire 1994; Squire et al., (2004). It is assumed that the declarative memory system is based on explicit information that is easily accessible and is concerned with specific facts or data. It includes episodic and semantic representations of propositions and images. On the other hand, the non-declarative memory system is based on implicit information that is not easily accessible and includes unaware representations of motor, perceptual, and cognitive skills, as well as priming, simple classical conditioning, and non-associative learning. In this model the hippocampus and interconnected neural regions, such a perirhinal cortex, postrhinal/parahippocampal gyrus, and entorhinal cortex encompasses the medial temporal lobe and is assumed to be the critical neural substrate in mediating all forms of memory within the declarative memory system. The non-declarative memory system include the mediation of skills and habits by the striatum, priming by the neocortex, simple classical conditioning of emotional responses by the amygdala, simple classical conditioning of skeletal musculature by the cerebellum, and non-associative learning by reflex pathways. It is assumed that the two memory systems are independent of each other. The Squire model assumes that the declarative memory system is based on conscious awareness and is involved in consolidation, and that all the areas of the medial temporal lobe are critical for recollection of all types of sensory information. In the tripartite attribute model, I do not differentiate the attributes on the basis of conscious awareness. Furthermore, I assume that for each attribute, the same processes operate in the event-based memory system and thus, non-declarative memory does not operate in the tripartite attribute model. In my model, the perirhinal cortex and hippocampus subserve different attribute information, such as object information and spatial information, respectively. Also, the prefrontal cortex is not usually incorporated in a specific memory system and the emphasis has not been on the different attributes of memory.

Cohen and Eichenbaum, (1993) and Eichenbaum (2004) propose that the declarative memory system is dependent upon the hippocampus, which provides a substrate for relational representation of all forms of memory including conjunctive, configural and arbitrary associations, as well as representational flexibility allowing for the retrieval of memories in novel situations. In contrast, the procedural system is independent of the hippocampus and is characterized by individual representations and inflexibility in retrieving memories in novel situations. In contrast to this declarative/non-declarative perspective, relational memory theory proposes that the medial temporal lobe function is independent of conscious awareness.

Recently, a model for episodic recognition memory was proposed and extended by Eichenbaum et al, (2007). This model, called the Binding Items and Context (BIC) model, proposed that information pertaining to item identity (i.e., “what”) resides primarily in the perirhinal cortex and information pertaining to the context wherein an item was experienced (i.e., “where”) resides primarily in the postrhinal/parahippocampal cortex. The item and context information are transmitted through the lateral and medial entorhinal cortices, respectively, and they enter the hippocampus, at which point the item and the context are bound together into coherent episodes. I assume that for each attribute the same processes operate in the event-based memory system and thus, the different components of the medial temporal lobe utilize similar functions. Even though Eichenbaum is clearly aware of the importance of the prefrontal cortex in its interactions with the hippocampus, the prefrontal cortex is not included in the overall memory model.

Summary

Memory is a complex phenomenon due to a large number of potential interactions that are associated with the organization of memory at the psychological and neural system level. Thus, it is not surprising that there many different models of memory (see other theories of memory section). In the Kesner tripartite, multiple attribute, multiple process memory model, different forms of memory and its neurobiological underpinnings are represented in terms of the nature, structure, or content of information representation as a set of different attributes including language, time, place, response, reward value (affect), and visual object as an example of sensory-perception. For each attribute, information is processed in the event-based memory system through a variety of operations but especially for short-term and intermediate-term memory and pattern separation based on orthogonalization of specific attribute information. In addition, for each attribute, information is processed in the knowledge-based system through a variety of operations, but especially for long-term storage and perceptual memory. Finally, for each attribute, it is assumed that information is processed in the rule-based memory system through the integration of information from the event-based and knowledge-based memory systems for the use for major processes that include especially short-term or working memory and paired associate learning. The neural systems that subserve specific attributes within a system can operate independent of each other, even though there are also many possibilities for interactions among the attributes. Although the event-based and knowledge-based memory systems are supported by neural substrates and different operating characteristics, suggesting that the two systems can operate independent of each other, there are also important interactions between the two systems, especially during the consolidation of new information and retrieval of previously stored information. Finally, because it is assumed that the rule-based system is influenced by the integration of event-based and knowledge-based memory information, there should be important interactions between the event-based and knowledge-based memory systems and the rule-based memory system. Thus, for each attribute, there is a neural circuit that encompasses all three memory systems in representing specific attribute information. In general, the tripartite attribute memory model represents the most comprehensive memory model capable of integrating the extant knowledge concerning the neural system representation of memory.

It is important to note the new information that has been obtained during the last decade. For the event-based memory system there has been (a) an extensive elaboration of the temporal attribute in terms of memory for duration, memory for sequential spatial processing, and memory for the sensory-perceptual attribute based on the operation of short-term memory or working memory, and (b) an emphasis on the characterization of an attribute-based pattern separation process. For the knowledge-based memory system, only a few studies have been added. For the rule-based memory system there has been a more elaborate detailed delineation of the different subregions of the prefrontal cortex in mediating working memory and paired associate learning, as well as a discussion of potential interactions between the event-based and rule-based memory systems.

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How to Reference This Article:

Kesner, R. (2013). Neurobiological Foundations of an Attribute Model of Memory. Comparative Cognition & Behavior Reviews, 8, 29-59. Retrieved from https://comparative-cognition-and-behavior-reviews.org/index.html doi:10.3819/ccbr.2013.80003

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Contact Information:

Raymond Kesner
Department of Psychology
University of Utah
Salt Lake City , Utah
Phone: (801) 581 7430

What hummingbirds can tell us about cognition in the wild

Susan D. Healy,
University of St. Andrews, St. Andrews, Fife, UK

T. Andrew Hurly,
University of Lethbridge, Lethbridge, Alberta, Canada

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Abstract

Here we review around 20 years of experimental data that we have collected during tests of cognitive abilities of free-living, wild rufous hummingbirds Selasphorus rufus at their breeding grounds in southwestern Alberta. Because these birds are readily trained to feed from artificial flowers they have proved a useful system for testing cognitive abilities of an animal outside the box wherein animal cognitive abilities are so often tested in the laboratory. And, although these data all come from a single species in a single location, the long-term aim of this work is to make a contribution to our understanding of the evolution of cognitive abilities, by examining the relationship between the ecological demands these birds face and their cognitive abilities. Testing predictions based on our knowledge of their ecology we have found that, while these birds aggressively defend a territory and display to females during the time we train and test them, they can learn and remember the locations of rewarded flowers, what those flowers look like, and when they are likely to contain food. Small-brained though they may be, these 3g hummingbirds appear to have cognitive capabilities that are not only well matched to their ecological demands, they are in at least some instances better (more capacious) than those of animals tested in the laboratory.

Keywords: hummingbird; field research; spatial orientation; timing; choice

Acknowledgements: We thank Rachael Marshall for helpful comments on an earlier version of manuscript, NSERC for funding T.A.H. and all of the cabin team for their contributions to this work. Corresponding author: susan.healy@st-andrews.ac.uk All previously published figures and photographs are used with permission of the authors.

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Around 15 years have passed since the publication of Animal Cognition in Nature (Balda, Pepperberg, & Kamil, 1998). Ironically, this was a volume that did not, in fact, actually contain any chapters examining the cognitive abilities of animals in the wild. It did, however, contain descriptions of work on wild animals trained and tested under laboratory conditions and seemed to herald a major expansion of work on comparative cognition to encompass a much wider range of species than previously tested. A decade and a half later, however, it is not clear that that promise is being realised. For example, food storing, once a model for examining questions of the evolution of cognition and possibly the wildest of all the examples discussed in Balda et al. (1998), is now much less of a focus (e.g., Biegler, McGregor, Krebs, & Healy, 2001; Hampton & Shettleworth, 1996; Sherry & Vaccarino, 1989; but see Feeney, Roberts, & Sherry, 2009; Freas, LaDage, Roth, & Pravosudov, 2012). Food storing did, however, lead to perhaps the greatest recent flurry of excitement and effort in comparative cognition (Clayton & Dickinson, 1999): the examination of cognitive abilities in corvids. Subsequent work is now ranging from examination of episodic-like memory in a number of species including rats (Babb & Crystal, 2005), magpies Pica pica (Zinkivskay, Nazir, & Smulders, 2009), chickadees Poecile atricapillus (Feeney, et al., 2009), hummingbirds (Henderson, Hurly, Bateson, & Healy, 2006a), and meadow voles Microtus pennsylvanicus (Ferkin, Combs, Delbarco-Trillo, Pierce, & Franklin, 2008) to examination of problem-solving in a variety of contexts, typically by corvids but not always (Auersperg, Huber, & Gajdon, 2011; Dally, Emery, & Clayton, 2010; Schmidt, Scheid, Kotrschal, Bugnyar, & Schloegl, 2011; Taylor, Elliffe, Hunt, & Gray, 2010; Teschke & Tebbich, 2011; Weir, Chappell, & Kacelnik, 2009).

In fact, much of comparative cognition can be comfortably addressed in the laboratory, even when wild animals are tested. This may help to explain why there continues to be very little examination of cognitive abilities of animals in the wild, in what might be considered to be the real world. That world is one in which animals are faced daily with getting food, finding mates, avoiding predation, and this is where selection acts on cognitive abilities, perhaps favouring animals that are generally smart or, alternatively, favouring animals that are good at solving particular problems. The questions, then, differ slightly from those asked of animals in a laboratory i.e., not just what animals can do but what and how do they put those abilities to work when the test itself does not occupy much of their day. It is possible that we will find that animals’ cognitive abilities in the field differ little or not at all from those we see in the laboratory. For example, the use of food deprivation in the laboratory to motivate animals to perform a test may resemble the state in which many wild animals find themselves i.e., often hungry and very willing to work for reliable food rewards. On the other hand, having to watch out for predators or competitors may mean that animals attend to experimental features differently than if they were to be tested in the field or the spatial scale over which testing occurs (Figure 1). Natural conditions might also lead to different cue use or different cue weighting than we see when animals are tested in boxes, arenas or (relatively) small rooms in the laboratory.

Going out into the field to test cognitive abilities certainly shares problems with laboratory tests, not least of which is being sure that the animal ‘answers’ the question experimenters think they are asking. If an animal fails to respond in an experiment, for example, it is frequently unclear whether this is because the animal is not motivated to respond or does not ‘know’ how to respond. Only when the animal does make a response that seems vaguely appropriate can we begin to measure its performance. Even then, variation in its performance may be due to motivation rather than to cognitive ability per se. In the field the animal may be distracted mid-test or simply fail to return to the test after failing to find a reward. A second major issue that has arisen with recent tests of cognitive ability in the wild is with the ‘unit of measurement’ for cognitive ability, especially when problem solving is that measure. Thus far, we are not aware of a general consensus as to what constitutes a problem or what makes one problem more difficult than another. For example, currently, manipulation of physical material to retrieve food from manmade devices is considered by some to require ‘complex’ cognition although an apparently similar manipulation of materials to build a nest, however complex, is not (Seed & Byrne, 2010; but see Muth & Healy, 2011; Walsh, Hansell, & Healy, 2010; Walsh, Hansell, Borello, & Healy, 2011; van Casteren, Sellers, Thorpe, Coward, Crompton, Myatt, & Ennos, 2012). Is a problem considered more difficult if it has more steps to the solution, even if each step is ‘easy’, or is a problem more difficult if it is more novel, either in appearance or in its solution? It would seem that there is a problem in using problem solving as a measure for cognitive abilities in animals. And if we have no measure that is readily quantifiable, then it will not be possible to determine the causes or consequences of variation in that measure, within or across species.

It will come as no surprise that we have not attempted to examine problem solving in our work examining the cognitive abilities of rufous hummingbirds, trained and tested in the wild at our field site in the eastern Rocky Mountains, Alberta, Canada. At least, we have not looked at their ability to manipulate tools or to solve problems of the kinds that crows and others are now being set. Rather we have required our birds to learn to feed from all manner of devices (Figure 2), which they have invariably been very quick to do, typically learning within a couple of hours where to insert their tongue to receive sugar solution (sucrose). Although some might say that speed of learning in itself indicates cognitive ability (e.g., Boogert, Fawcett, & Lefebvre, 2011; Keagy, Savard, & Borgia, 2012), the fact that these birds learn so readily has for us largely meant that they are a useful species for examining cognition in the field: animals that took 100’s or 1000’s of trials to learn how to solve a task would have led us to look for other species. Here we review our work with two aims in mind: (1) to show that basing an experimental framework on knowing the ecology of a species can lead to a useful understanding of that species’ cognitive abilities, and, (2) in light of the paucity of work done in the wild, we want to use our work on rufous hummingbirds as a case study to show what is possible to do in the messiness of the field, where our control over the animal’s behaviour and experience is compromised. We would hope to show that such a pursuit can be both fruitful and that by doing so we add usefully to our understanding of cognition acquired from laboratory experiments. By so doing we would hope to encourage others similarly to go out into the field to examine cognition in other species. If we want to understand the evolution of cognitive abilities, especially in the vertebrates, the answers will not come from work on a single species, irrespective of the depth of enquiry.

Interested as we are in comparative cognition, we have two significant reasons for attempting to determine the cognitive abilities in a single species, specifically rufous hummingbirds, in the wild. Firstly, these birds are logistically amenable to testing. As described elsewhere (e.g., Healy & Hurly, 2003, 2004), the males (the focus of our efforts) are strongly territorial, excluding conspecifics from feeding and thus from being trained to use our experimental equipment, they can be readily marked for individual identification and they feed every 10-15 minutes throughout the day for the duration of the breeding season (Figure 3). Although one might say that cognition is studied in the laboratory for logistic reasons such as experimental and experiential control, the choice of a species to test in the wild is, at this relatively early stage of such work, crucial to success. For example, the fact that our hummingbirds feed every 10-15 minutes means that we can collect a useful amount of data within a day and across our six-week field season. Choosing to work with animals that feed once a day or less often, would lead to major issues with training animals and collecting enough data, without each study taking a lifetime!

Equally key to the success of the endeavour is that the behaviour and ecology of these birds is such that we can readily formulate predictions as to the nature of the birds’ cognitive abilities from our observations of the birds’ foraging behaviour. Rather than using a rather arbitrary task to examine their cognitive abilities, we can attempt to test those abilities we would expect might have been favoured in these particular animals. Foraging behaviour in the male rufous hummingbirds, at least, typically consists of a male flying approximately every 10 minutes from a conspicuous perch in his territory to feed (from flowers or our feeders; Figure 4) for a handful of seconds before returning to his perch. The intervening time before his next foraging bout can be filled with a considerable activity as he is constantly on the lookout for conspecific males and females. Territorial males display to conspecific rivals by the flashing of their bright orange gorget (throat) feathers. If this does not deter an intruder, it will be chased off at high speed. Females are also chased, especially off the feeders, but they tend to move to a position near the ground while the male performs several display flights. These consist of the male flying up (some 15m) and then flying steeply downwards before pulling out of his dive just above the head of the female, flying a short upward sweep and ending with a waggle (a short series of oscillations in the vertical plane). He then either repeats this manoeuvre several times or flies to the female and performs a shuttle-flight – a series of short zig-zag buzzing flights in front of her. The aim of this game is to persuade the female to mate (Hurly, Scott, & Healy, 2001). Although we have not measured the energy expenditure of the males’ various flight acrobatics, it appears that they would be energetically expensive (Clark, 2009). Indeed, males tend to visit the feeder (flowers) within a few minutes of such displays, although their visits are still no longer than a few seconds. Our very first speculation with regard to their cognitive abilities, then, was that this small (about 3g) nectarivore, defending several hundred (or more) flowers and feeding about every ten minutes for a few seconds only, might benefit from remembering which flowers he had recently visited (Healy & Hurly 2001). Not only would he save time and energy by remembering where they were, it should be useful to remember whether he had emptied the flower(s), or not. A bird that could do this would return to territory defence and mate attraction more quickly having expended less energy.

The success of our very first, speculative experiment set the scene for most of the experiments that have followed. In that first experiment, we presented rufous hummingbirds with an open-field analogue of a radial-arm maze: an array of artificial flowers, some of which contained a small amount of sucrose solution (Healy & Hurly, 1995). The flowers were coloured cardboard discs approximately 6cm in diameter, each glued to the end of a wooden stake 60cm tall (Figures 2 and 5). They were arranged in a rough circle with about 70cm between neighbouring flowers. For this experiment, the flowers held 40μl, an amount that meant the birds should visit and drink all of the contents of about four of the eight flowers. We presented the birds with two versions of this delayed-non-matching-to-sample task: in one version, all eight flowers contained reward but we allowed birds to visit only up to four flowers on their first visit to the array and in the second version, all eight stakes were presented but only four bore flowers. For both versions, then, birds visited and emptied up to four flowers of their contents. On their return to the array, after intervals ranging from five minutes up to an hour, all eight flowers were present but only the flowers that had not been visited in the first phase of the trial contained food. As predicted, the birds were much more likely to visit flowers they had not recently emptied. A follow-up experiment showed that birds were also more likely to visit the flowers that had been present when they first visited the array but from which they did not drink (Hurly, 1996; see also Henderson, Hurly, & Healy, 2001). Although memory for perhaps as many eight flowers is not in the ball park of the number of flowers thought to make up the territory of these birds (perhaps a couple of thousand), the birds could remember not just where the flowers were but that they had emptied them.

That the birds could remember something about a flower’s contents was confirmed by an experiment in which we were actually aiming to address the role that flower colour played in the birds’ ability to learn which flowers to visit. We expected that the birds would pay attention to the colour of the flowers both because, like us, birds have well-developed colour vision and there is much anecdotal evidence that hummingbirds are attracted to red objects. There was also speculation that this predilection for red had lead to the propensity for the Californian flora, which lie on the migratory path of these birds, to produce red flowers. In that experiment we presented the birds with four, individually coloured flowers, only one of which contained sucrose solution and too much for a bird to consume in one visit. Once a bird had found the rewarded flower and then left after drinking as much as he wished, we emptied the flower and switched it with one of the other flowers in the array. When the bird returned, he was more likely to go to the flower that was in the location of the flower he had most recently fed from, rather than to the flower with the colour of the earlier, rewarded flower (Hurly & Healy, 1996; Miller & Miller, 1971; Miller, Tamm, Sutherland, & Gass, 1985). Consideration of the nature of the birds’ ecology helps to explain why these bird seemed to ignore the colour cue provided by the flower: in a field of flowers of the same species, colour does not help the bird determine which flowers will be rewarding. Colour might, however, be used to find flowers in unfamiliar places, such as along a migratory route and red may well be more conspicuous against a background of browns and greens that make up a western North American mountain range. It has also been argued elsewhere that the ubiquity of red flowers along the migration route of the rufous has less to do with attracting hummingbirds than making flowers inconspicuous to insects, whose vision is poorer at longer wavelengths (Altshuler, 2003; Briscoe & Chittka, 2001; Raven, 1972).

The longer we have experimented with these birds, the more we have found evidence for their ability to learn information as is necessary, because we have found they will learn and remember the colours of flowers, we just had to ask them in the appropriate fashion. Two examples will illustrate this. Firstly, in an experiment in which we were primarily interested in the accuracy with which they could remember a location, some birds were trained that yellow flowers were rewarded while others were taught that red flowers were rewarded. This colour-reward association was learned within 2-3 experiences (Hurly & Healy, 1996). Secondly, we trained birds that three of the flowers in an array of ten contained sucrose solution. All of the flowers differed in their colour pattern. Once the birds had learned which were the rewarded flowers we moved the array 2m from the site of the original array so the birds could not use the location of the flowers to determine which were the rewarded flowers. However, it was not until we had also changed the shape of the moved array that we found that the birds had remembered the colours of the flowers rewarded in the first array (Hurly & Healy, 2002; Figure 6). The birds can and will learn and remember colour but our ability to demonstrate that they can and will do so required us to be much more particular about our experimental designs. We would have been both remiss and incorrect if we had concluded that rufous hummingbirds were unable to learn colour cues, a very ubiquitous cognitive ability.

Not only do we have to be particular about our experimental designs, we also have to be careful about our expectations of what these birds may or may not be capable. Expectations of animals’ cognitive abilities tend to come from two sources, some based on knowledge of the animals’ ecology and others from what might loosely be described as being based on their brain size. Rufous hummingbirds weigh around 3g and, although they have a brain that is larger than expected for their body size (Ward, Day, Wilkening, Wylie, Saucier, & Iwaniuk, 2012), that brain is still not very large. Knowledge of the birds’ ecology leads to expectations that these birds might, for example, pay more attention to spatial information than to colour information (but that they would still pay attention to colour in the relevant contexts), while their brain size might lead to expectations of noticeable limits to the capacity for and speed and the accuracy with which the birds learn spatial locations. We are familiar with onetrial learning from the retrieval successes of food-storers and from long-delay taste aversion learning but even in tasks where animals are highly motivated to learn locations such as in rats searching for hidden platforms in the Morris water maze, animals often either take several trials to learn a location or require some time exploring the location in a first visit. Like food-storing birds, rufous hummingbirds, however, learn the three-dimensional location of a reward from a single visit that lasts only a few seconds. They can return to that location even in the absence of the flower (Flores Abreu, Hurly, & Healy, 2012) and they can visit several such ‘empty’ locations. Usefully, hummingbirds can demonstrate their memory for a rewarding location in the absence of the local cues of that reward because they will fly to, and then hover at, that location, much as a rat in a water maze will swim back and forth over the place it has learned to find a hidden platform.

Although we have not measured the accuracy with which the birds can return to a flower that has been removed after a single visit, we have attempted to measure the 3-D accuracy of memories for a familiar location. We trained birds to fly to a rewarded flower (a red 8cm³ cardboard cube) in a large featureless field, then removed the flower and filmed the bird’s flight path into the location of the now-missing flower. The birds flew to within 60cm in the horizontal plane and within 20cm in the vertical plane (Hurly, Franz, & Healy, 2010). They did not appear to beacon to the flower in spite of the ‘flower’ being highly conspicuous, as when we simply moved the flower about 1.5m, the birds flew nearer to the location of the missing flower, and hovered, before flying directly to the moved flower. The birds’ accuracy for a flower’s location seems to depend on the size of the flower. In a second experiment, we trained birds to feed from either a small (8cm³) or a large flower (1000cm³). This time, in the absence of the flower, the birds flew even closer to its previous location than they had in the earlier experiment (the locations were not the same in the two presentations): 20cm in the horizontal and around 5cm in the vertical when the flower was small and around 50cm in the horizontal and about 25cm in the vertical when the flower was large. These data finally allowed us to confirm the precision with which a hummingbird can return to a learned but absent reward described in the many anecdotes of hummingbirds returning to sites of feeders they had fed from during their last migration or breeding season. The appearance of birds at particular windows of houses is a common incentive for people to get feeders out of the cupboard after the winter.

One obvious difference for the birds in our experiments from the birds in these reports, however, was that we deliberately chose to place the experimental flower at least 10m from any obvious landmarks (e.g., bushes, trees; Figures 1 and 5). The data from experiments on various species in the laboratory would suggest that the birds might have learned the flower’s location in one of three ways: they may have learned the visual characteristics of the flower and used it as a beacon, they could have used the landmarks proximal to the flower or, they used a number of distal landmarks. The behaviour of our real-world animals, however, does not readily conform to any of these three possibilities: while they can use the flower as a beacon, as shown by the birds flying to the moved flower once they discover the one in the familiar location is missing, they do not need to beacon to the flower and they do not do so preferentially. Graham, Fauria, & Collett, (2003) suggested that their ants might use large landmarks along a route as beacons while learning that route and that those landmarks might act as a scaffold for learning other landmarks nearby so that the animals could move along the route if the beacons were then removed. While this seems plausible for our hummingbirds it is not at all clear which landmarks along the way would have formed this scaffold. The proximal landmarks were (to our eyes, at least) remarkably uniform: the ground was quite flat, and covered by vegetation that reached perhaps 20cm punctured by multiple ground squirrel burrows. The distal landmarks, on the other hand, were very conspicuous and ranged from trees, typically ringing the open fields used for training and testing, to the mountains rising some 1000m along both sides of the valley and visible from all points in the open fields. However, it was still not clear how such large landmarks would enable the birds to be quite so accurate in their return to the flower location. It is possible that the birds did not use visual cues at all as they may well have used magnetic or sun compass cues instead but there is no reason to believe that such cues would be better at supporting accuracy than are distal visual landmarks. We have no useful information on the use of either of these systems in hummingbirds although there is an abundance of data for their use by other birds so it seems at least plausible that hummingbirds might also use them. We also cannot reject the possibility that the birds did use either proximal or distal visual landmarks simply because we cannot yet determine which they might have used or how they might have used them.

Surprisingly, these data on the memory that hummingbirds have for rewards located in three-dimensional space are among only a handful of data on spatial cognition in 3-D (Grobéty & Schenk, 1992; Holbrook & Burt de Perera, 2009; Holbrook & Burt de Perera, 2011). Although all flying and swimming animals live their lives moving through 3-D space, even terrestrial animals can and will move through the vertical dimension of their world and yet, we know almost nothing about whether the z-dimension is encoded differently from the way in which x and y dimensions are encoded, whether the animals pay attention to the dimensions differently or whether either or both of these are dependent on whether the animal moves through the z-dimension. Rufous hummingbirds might remember flowers bettewr if they differ in their height (Henderson, Hurly, & Healy, 2006b) and they may also remember a 3-D location more accurately than do rats, when both species have learned a location in a 3-D maze (Flores Abreu, 2012). Whether these outcomes are due to greater familiarity with moving through 3-D space or to an ability that has been favoured by natural selection is not yet clear and requires investigation of 3-D spatial performance in more species.

That performance depends on the way in which the question is asked is demonstrated yet again because hummingbirds apparently encode vertical information less accurately than horizontal information when the locations to be discriminated differ only in their vertical component: when flowers were presented on a vertical pole (Figure 7), birds found it difficult to learn which one of five flowers was rewarded but when the flowers were presented along a diagonal pole, the birds were relatively quick to learn which was the rewarded flower (Flores Abreu, Hurly, & Healy, 2013). Here it appears that the addition of a horizontal component to the flower’s location may have facilitated the learning of its vertical location. Additionally, when presented with only two flowers the location of which differed only in the vertical component, hummingbirds appeared to learn which was the rewarded flower relative to the other i.e., whether the flower was the upper or the lower of the two flowers (Henderson et al., 2006b).

Unlike 3-D spatial cognition, a considerable amount of work has been conducted on a variety of species into the ways they learn and use time. Interval timing over short time periods has been well studied in the laboratory while circadian timing has been much investigated in both the laboratory and the field (e.g., Sylvia borin, Biebach, Falk, & Krebs, 1989; pigeons Columba livia, Saksida & Wilkie, 1994; hamsters Mesocricetus auratus, Cain, Chou, & Ralph, 2004; and bees Apis mellifera, Pahl, Zhu, Pix, Tautz, & Zhang, 2007). More recently, investigations into episodiclike memory (often also called what-where-when memory) have also raised questions as to what kind of time constitutes the ‘when’ component of this kind of memory. There are a priori reasons to suppose that rufous hummingbirds might also be capable of using more than one kind of time. For example, it has been suggested that territorial hummingbirds, like the rufous, use defence by exploitation as a means to exclude nectarivorous intruders and that this is effected by the territorial holder feeding on, and thereby emptying, the flowers at the edge of his territory early in the day (Paton & Carpenter, 1984). Traplining, whereby an animal moves around a circuit of resources (such as flowers) in a predictable pattern and time period, may also be used by foraging hummingbirds that are using their floral resources effectively (Feinsinger & Colwell, 1978). Finally, for hummingbirds foraging on flowers that refill their nectar supplies (perhaps within a few hours), being able to remember which flowers they visited and when, would enable more effective foraging. The biology of these birds, then, suggests that they might be capable of circadian timing, sequence learning and/ or interval timing.

Given the possible daily requirement for remembering when flowers had been emptied, we began investigating the hummingbirds’ ability to learn time intervals by presenting a bird with an array of eight, individually distinctive flowers, four of which were refilled 10 minutes after the bird had emptied them and four were refilled 20 minutes after being emptied. Each flower had contained the same amount and concentration of sucrose so the time to refill was not an indicator of a flower’s contents and the amount in a flower was such that the bird would typically visit 3-5 flowers per visit to the array. However, he could visit all the flowers or only one, the number of flowers and which flowers to visit were chosen by the bird. The array was presented in the morning and the territory owner was allowed to visit at will throughout the day. The flowers were removed overnight and replaced in the same place the following day. As we did not know how long it would take for a bird to learn the refill rates (if at all), we presented each of three territorial birds with an eight-flower array for 11 days. It turned out that the birds had, in fact, learned the refill rates quite well by the end of the first day (our expectations of their performance vastly underestimated their abilities) but the key finding was that all three birds returned to the 10-minute refilling flowers at around 10 minutes and to the 20-minute refilling flowers at around 20 minutes (Henderson et al., 2006a). Although these time periods are much shorter than typical refilling times for real flowers and birds may defend up to a couple of thousand flowers in their territories, we considered that the Henderson et al. (2006a) data at least showed ‘proof of principle’ and that these birds could learn refill rates, for multiple flowers and relatively quickly. Furthermore, not only were the duration of the refill rates longer and the number of flowers that the birds could track more than has been tested in the laboratory, all of the birds were living a very active life throughout their foraging on the array, defending their territory from intruding males and displaying to females. One suspects that if these birds were tested in the more controlled environment of a laboratory, their abilities would seem even more impressive. Rather than testing them in the laboratory, we then looked to see whether, if the colour of the flower signalled the refill rate, the birds would use that colour and learn the refill rate of the flower more quickly than if there was no colour-refill association. This was one of the instances in which we based our expectation of what the birds would learn on the information we would be likely to use ourselves: if the 10-minute refilling flowers were blue and the 20-minute refilling flowers were pink, for example, it would seem that the colour might reduce the time taken to learn the refill schedule. However, it did not affect the speed at which the birds learned which flowers refilled after 10 minutes and which refilled after 20 minutes (Marshall, Hurly, & Healy, 2012; Figure 5). Indeed, there is some preliminary evidence that colour-refill associations may also not affect the speed with which people learn the duration before which flowers refill (Marshall, 2012). In a second experiment reported by Marshall et al., (2012), birds also did not learn which flowers held 20% sucrose solution and which held 30% sucrose sooner when the flower colour signalled the sucrose concentration. Of course, our earlier data whereby birds did not appear to learn the colour of a flower when its location did not change should have alerted us to the probability that colour would also not be salient to the birds in the Marshall et al. (2012) experiment, or at least, not as salient as is space. It appears that space overshadows colour information in a range of contexts. One way to test this would be to move the flowers after each visit. In such a manipulation, space would no longer be a reliable cue and colour would be.

Having found that the hummingbirds could learn refill rates, and in experiments on risk sensitivity and context-dependent choice, that they readily learn what (volume and concentration of sucrose) is held in flowers or wells of different colours, it was clear that these birds could learn and remember each of the three (what-where-when) components of episodic-like memory (Hurly & Oseen, 1999; Bateson, Healy, & Hurly, 2002; 2003; Morgan, Hurly, Bateson, Asher, & Healy, 2012). They can also remember pairs of these components: they can remember when and where (Henderson et al., 2006a) and they can remember what and where (e.g., Healy & Hurly 1995; 1998) although we have not explicitly looked for whether they can remember what and when together. The question then was whether they could remember all three components together. Although there is now considerable evidence that a range of animals have episodic-like memory, including rats (Babb & Crystal, 2005), magpies (Zinkivskay et al., 2009), chickadees (Feeney et al., 2009), and meadow voles (Ferkin et al., 2008), issues over designing an appropriate experiment continue to plague this field. The most systematic difficulty concerns the ‘when’ component, with differing groups defining this in different ways (e.g., including a place in a sequence: Ergorul & Eichenbaum, 2004; a time of day: Zhou & Crystal, 2009; and using “which” instead of “when”: Eacott, Easton, Zinkivskay, 2005; Eacott & Norman, 2004). Common to all of these approaches, however, is that the test would allow animals to demonstrate their ability to remember what, where and when in combination. We took a slightly different approach again, which was to design an experiment in which we could explicitly examine all of the components of whatwhere and when memory. We did this by presenting birds with a pair of four-flowers arrays in which the single flower that contained reward differed for the different times of day at which the arrays were presented. The distinctive colours of the four flowers in each array (e.g., blue, yellow, purple, and red; Figure 8) occurred in the same relative positions for both arrays. The arrays were presented in the morning and a territorial male was allowed to search around the flowers to find the rewarded flower. He was then allowed to return to the arrays to feed from the rewarded flower for a further five visits before the arrays were removed. The arrays were presented again in the afternoon and the bird had again to find the rewarded flower. In this latter array presentation, the rewarded flower was in the other array and of one of the other colours. After five subsequent visits to the rewarded flower, the arrays were removed. They were presented to the bird over the next few days at approximately the same times each day. Over the course of these presentations we found that birds visited the eight different flowers differently: there was a single ‘correct’ flower, which was in the correct array at the correct time and of the correct colour, one flower that was in the correct array and of the correct colour but at the wrong time, one flower that was at the correct time and of the correct colour but the wrong array, three flowers that were in the correct array at the correct time, and two flowers that were wrong array, at the wrong time and of the wrong colour. The most common flower the birds chose was the rewarded flower and they went least frequently to the flowers that were completely wrong (Marshall, 2012). Of the other kinds of flowers, relative to chance, the birds went most often to the flower that was rewarded at the other time of day, which is consistent with the data from episodic-like memory tests where it is the when component that is the most difficult for the animal to get right. Furthermore, the birds most readily corrected what errors as they typically flew from a what error flower directly to the correct flower. These outcomes are consistent with what we have seen in other experiments with the birds: they have good spatial memory and they seem to remember colour only when space is not relevant. Although this experiment was not an episodic-like memory experiment as the birds visited the arrays six times at each time period and the trials were not trial-unique, this kind of experimental design might enable a comparison across species in their episodic-like memories of how well they can remember each of the three components. For example, do other animals remember the where better than the what and the when as do hummingbirds? Might a species’ ecology enable us to predict the pattern of variation in the structure of episodic-like memories (if there is one)? It might also help to determine which species serves as the most useful model of human episodic memories. The data from this experiment also suggest that, in addition to being capable of learning intervals, the birds can learn circadian time.

One-trial learning of 3-D locations, accurate spatial memory and timing capacity: these animals have cognitive capabilities that appear to match their ecological requirements, although we have not actually tested the extent of these abilities (e.g., whether the birds can remember most of the flowers in their territories, rather than the handful on which we have tested them). For us, this raises multiple questions, such as how specific are these abilities? Is it the case, for example, that other animals are also capable of this kind of timing capacity or has natural selection favoured this particularly in the hummingbirds and only in other species that have faced similar cognitive challenges in their foraging strategy (or in some other part of their lifestyle)? This question requires comparative data, of the kind gathered to address similar questions asked of the capacities of food storers relative to nonstorers, of sticklebacks living in different environments, and a handful of other species (Girvan & Braithwaite, 1998; McGregor & Healy, 1999; Odling-Smee, Boughman, & Braithwaite, 2008).

Accurate spatial memory and timing capabilities allow rufous hummingbirds to make appropriate decisions about which flowers to visit and when and we have used their ecology to make predictions about their cognitive capabilities. We have also used data from the literature on human decision-making to ask questions about whether or not these birds make so-called irrational decisions. A rational decision is one in which the animal/human faced with an array of options is expected to choose the option that obtains the highest utility. For animals, the utility of a foraging option is considered to be the one that provides the highest energetic return. For humans, utility might be measured in terms of energy, finance, or some other useful currency and humans were also always assumed to make rational choices. However, there is now a wealth of data to show that humans do not always make rational decisions and increasing evidence to show that animals, including rufous hummingbirds, also do not make such decisions (Bateson et al., 2002; 2003; Hurly & Oseen, 1999; Latty & Beekman, 2011; Morgan et al., 2012; Sasaki & Pratt, 2011; Scarpi, 2011, Waite, 2001). One of the experimental paradigms used to show that rufous hummingbirds are irrational consists of offering birds a choice between two favourable options (a binary choice set; Figure 2) and a choice among three options, the two favourable options from the binary choice set plus a third poorer option. If the birds were choosing rationally, the inclusion of the poorer option should not affect the relative preference the birds have for the two favourable options, but it does (Bateson et al., 2002; 2003; Morgan et al., 2012). One suggested mechanism for this change in preference is that sampling of the poorer option forces birds to take relatively more of the better of the two favourable options to regain the lost energy. However, the hummingbirds do not always respond to the poorer option by increasing their preference for the option with the highest energy return (Morgan et al., 2012). Another possibility is that the birds assess the options available relative to each other rather than with respect to their absolute values. This might mean then, that the inclusion of a poor option in the choice set might make the better of two favourable options seem much better than when just the two favourable options are presented and so a bird should increase its relative preference for that better option. It might also mean that the inclusion of very poor option to the choice set would lead the bird to perceiving the two favourable options as more similar to each other, which would result in a bird choosing the two favourable options more similarly than it had when presented with the binary choice. Just this experiment is currently underway.

Why hummingbirds might make irrational decisions over foraging options is not yet clear. There are at least two possibilities that have been raised to explain irrational human decision making: 1) the birds are perceptually or cognitively constrained and are simply unable to measure each of the options sufficiently accurately to make the appropriate (rational) choice; 2) the birds are capable of measuring the available options but trade off making the perfect decision with the time taken to make the measurements as the costs of making the perfect decision at every foraging choice (for hummingbirds around every 10 minutes through the day) well outweigh the benefits of that decision. This latter seems intuitively more likely as no one foraging decision for these birds will be worth a bird spending a lot of time assessing the options. However, whether the frequent foraging for small meals means that these birds are more likely to make irrational decisions than are animals making more substantive decisions (in fitness terms), such as mate choice or offspring sex ratio is not yet clear. This is also a question that would be best addressed by a comparative approach, both within and among species. For example, rufous hummingbirds choosing mates may not trade off time with assessment accuracy because this is a decision that has medium to long-term fitness effects. Similarly, animals that feed on few but large meals as do many top predators may also be prepared to take longer to choose among foraging options in order to ensure they choose the option with the highest energetic return.

More comparative data would help to determine the role that cognition plays in the lives of animals and that is played by natural selection in producing variation in cognitive abilities. We are beginning to determine the cognitive capabilities of rufous hummingbirds when those animals are living a fast and furious life in the midst of our experiments. We consider that this work brings us considerably closer to understanding the use to which these animals could put their cognitive abilities than if we had conducted this work in the laboratory. In the real world, the benefits to cognitive abilities might range from the short term such as finding food for a single meal to the medium term such as managing to attract a good mate (Keagy, Savard, and Borgia, 2009), with the ultimate goal of the production of more and/or better offspring. Demonstration of these benefits requires not only the measurement of that ability, it also requires measurement of how variability in that ability maps onto a tangible benefit. However, although most assume that being ‘smarter’ must be beneficial, the data are remarkably sparse. Indeed, to our knowledge, perhaps the first data that might begin to confirm the smarter is better assumption have only just been published (Cole, Cram, & Quinn, 2012). Cole et al. (2012) showed that great tits (Parus major) brought into the laboratory for testing, that learned how to access food from a manmade dispenser, when returned to the wild, went on to lay more eggs over the following four years. These birds also managed to spend less time feeding their offspring. To show these effects Cole et al. (2012) tested, and then tracked in the field, over 400 individuals, a feat that many will find difficult to emulate. However, while laying eggs would suggest actual fitness benefits to greater problem-solving abilities, the solver great tits were also more likely to desert their nests than were those tits that did not solve the food access problem. In the medium term, then, problem-solving great tits did not produce more grand-offspring than did the nonsolvers.

We (collectively) have a way to go before we have good evidence for fitness benefits of cognitive abilities. However, we hope that our focus on our work on rufous hummingbirds shows that one can usefully address questions about cognitive abilities in wild, free-living animals. Furthermore, these data provide tentative evidence for natural selection acting on cognitive abilities: like food-storing songbirds and unlike non-food storing songbirds (e.g., Brodbeck, 1994), rufous hummingbirds place greater emphasis on spatial cues over colour cues, a hierarchy of cue preference that is correlated with their particular ecological demands. However, it remains to be seen whether other animals, living very different lives, will pay attention to time as do the hummingbirds. Finally, if natural selection has shaped cognitive abilities as it appears, then the next challenge will be for us to determine what cognitive capabilities hummingbirds lack.

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Social Cognition in Ravens

by Thomas Bugnyar,
University of Vienna, Austria

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Abstract

Complex social life has been proposed as one of the main driving forces for the evolution of higher cognitive abilities in humans and non-human animals. Until recently, this theory has been tested mainly on mammals/primates, whereas little attention has been paid to birds. Indeed, birds provide a challenge to the theory, on one hand because they show high flexibility in group formation and composition, on the other hand because monogamous breeding pairs are the main unit of social structure in many species. Here I illustrate that non-breeding ravens Corvus corax engage in sophisticated social interactions during foraging and conflict management. While Machiavellian-type skills are found in competition for hidden food, the formation and use of valuable relationships (social bonds) seem to be key in dealing with others in daily life. I thus argue that ravens represent a promising case for testing the idea that sophisticated social cognition may evolve in systems with a given degree of social complexity, independently of phylogeny.

Keywords: food caching and pilfering, perspective taking, conflict management, social bond, ravens, Corvus corax

Acknowledgements: This work was supported by the Austrian Science Fund (FWF; grants J2064, J2225; R31-B03, Y366-B17) and the European Science Foundation (ESF-EUROCORES framework TECT: I105- G11). Permanent support has been provided by the ‘Verein d. Förderer KLF’ and the Herzog von Cumberland Stiftung. I would like to thank the editor and three anonymous reviewers for their constructive comments on a previous version of the paper. Contact information: Thomas Bugnyar, Department of Cognitive Biology, University of Vienna, Althanstrasse 14, 1090 Wien, Austria; email: thomas.bugnyar@univie.ac.at

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Introduction: Why study social cognition in ‘moderately social’ birds?

Aside from problems related to foraging (Milton, 1988; Parker and Gibson, 1977), life in individualized social groups has been considered as one of the main driving forces for brain evolution (Jolly, 1966; Humphrey, 1976; Whiten and Byrne, 1988). Support for this ‘social brain’ hypothesis comes from studies correlating relative brain size with group size in non-human primates and in some other mammalian taxa (e.g. Dunbar, 1992; Dunbar, 1998; Pérez-Barbería, Shultz and Dunbar, 2007). In addition, field studies on primates and horses reveal effects of social competence on offspring survival (Silk, Alberts and Altmann, 2003; Cameron, Setsaas and Linklater, 2009) and some comparative studies in birds show that social species tend to outperform less social species in experimental tasks (e.g. Bond, Kamil and Balda, 2003).

Despite these encouraging findings, many open questions remain, notably about what constitutes social complexity and what type of cognition is selected for (e.g. Cheney and Seyfarth, 1990, 2007; Barrett, Henzi and Dunbar, 2003; Moll and Tomasello, 2007; Bond, Wei and Kamil, 2010). In birds and some mammals, for instance, brain size does not correlate with group size but with long-term partnerships (Dunbar and Shultz, 2007), indicating that dealing with a particular individual over time, rather than dealing with many individuals, may be cognitively challenging (see also Emery, Seed, von Bayern and Clayton, 2007). Furthermore, in a number of mammalian and avian species, groups are not cohesive units but frequently change in respect to size and composition, with individuals joining, leaving and re-joining (parts of) the group later (Tyack and de Waal, 2003; Emery, 2006). It has been proposed that such systems with high degree of fission-fusion dynamics are particularly challenging in cognitive terms, since individuals have to cope with the temporal absence of others, making it difficult to update information and knowledge about others (Aureli et al., 2008). Yet, empirical examples are focused on mammals such as primates (Amici, Aureli and Call, 2008), cetaceans (Mann, Connor, Tyack and Whitehead, 2000), carnivores (Holekamp, Sakai and Lundrigan, 2007), and elephants (McComb, Moss, Durant, Baker and Sayialek 2000; McComb, Moss, Sayialek and Baker, 2001).

In this paper I review recent studies on the common raven Corvus corax, a large-brained songbird renowned for its high ecological flexibility and scavenging foraging style (Ratcliffe, 1997). Compared with other corvids, it is usually characterized as a moderate social species, since adults defend breeding territories and are usually found in pairs (Boarman and Heinrich, 1999). However, those birds that do not have a territory – representing the class of non-breeders, i.e. young immature birds and sexual mature birds without a partner – tend to form groups that can be characterized by high fission-fusion dynamics (Heinrich, 1988; Marzluff and Angell, 2005). Group formation is advantageous in finding and accessing large but unpredictable food sources like carcasses and kills (Heinrich and Marzluff, 1991; Marzluff and Heinrich, 1991; Bugnyar and Kotrschal, 2002a). Probably because of benefits associated with foraging, non-breeder groups tend to be joined by adult pairs outside the breeding season and/or the year round, when no breeding territories are available (Braun, Walsdorff, Fraser and Bugnyar, 2012). Focusing on two aspects of social life, competition over food and the use of affiliate relations or social bonds, I argue (i) that the social system of ravens is more complex than previously thought and (ii) that this bird species represents a promising case for testing the idea that sophisticated social strategies and cognitive skills may evolve in systems with a given degree of social complexity, independently of phylogeny.

Social aspects of food caching

Food for thought.

Imagine a flock of ravens that have just found a dead moose: not all birds manage to feed because dominants try to monopolize those parts of the carcass that are not covered by skin and allow them access to the meat. One of those dominants flies off after a few minutes with its throat pouch filled with meat; another raven from the crowd flies off in same direction, but without any food. The raven with food does not go far but lands behind a rock in only a few hundred meters distance to the carcass. There, it puts down its piece of meat, sticks it into a crevice and covers the upper part with surrounding debris; it then stretches and turns its head, apparently visually scanning the area, before it leaves the cache and flies back to the carcass. As soon as it has left, another raven flies out of dense trees nearby, making a bee-line for the cache – it is the same bird that has left the carcass without food immediately after the dominant bird. Suddenly, a third raven appears – without food – from the direction of the food source. The raven approaching the cache immediately changes its direction and starts digging in the soil about 10m far from the actual cache. The newcomer lands there, displaces the digging ravens and starts digging, too; however, it soon gives up and flies back to the carcass. The remaining raven now flies directly to the cache, recovers the food and leaves with it in another direction.

Such a scenario is typical for group foraging ravens. When faced with competition for a large food source, they hardly eat but carry off consecutive loads of food for scatter hoarding at a moderate distance to the feeding site (Heinrich and Pepper, 1998). Other ravens may try to find and pilfer those caches, apparently taking into account the behavior of the cacher as well as the behavior of other potential pilferers. The competition for hidden food thus results in seemingly sophisticated maneuvers and counter-maneuvers from both, cachers and pilferers (Bugnyar and Kotrschal, 2002b).

Competition over hidden food

Caching of food for later consumption is a behavioral trait found in most corvids (ravens, crows, magpies and jays; de Kort, Tebbich, Dally, Emery and Clayton, 2006). As short-term cachers, ravens typically recover the food within a few hours up to a few days after the caches have been made. Notably, they tend to return to their caches when the other ravens have left the scene (Heinrich and Pepper, 1998). For group-foraging ravens, food caching may thus primarily reflect a strategy to secure food from conspecifics, rather than a strategy to save surplus food for later use. This stands in contrast to reports from some other corvids like Pinyon jays Gymnorhinus cyanocephalus, which routinely make caching trips in groups (Marzluff and Balda, 1992).

Competition for cached food, however, has been described for a number of corvids and appears to be based on memory for observed caches (Bednekoff and Balda, 1996a,b; Emery and Clayton, 2001). Experiments on captive ravens reveal that success in finding others’ caches is directly linked to the opportunity of observing them being made (Heinrich and Pepper, 1998; Bugnyar and Kotrschal, 2002b), whereas olfactory cues play no or only a minor role in locating hidden food (compare Harriman and Berger, 1986). This is corroborated by the fact that ravens have little problems in remembering up to 25 observed caches (Braun and Bugnyar, unpubl. data) and their memory for observed caches seems excellent for at least 24 hours (Heinrich and Pepper, 1998). Given that pilferers need to observe others making caches to be able to learn about the exact locations, pilfering is rarely a by-product of caching activities. Instead, ravens face the decision whether to try to get food directly from the source or to follow others leaving for caching to get a chance for pilfering (as described in the example above). Their choices can be modeled as a producer-scrounger game (Giraldeau and Caraco, 2000), with individuals caching food they got from the carcass acting as producers and those pilfering caches from others acting as scroungers. Importantly, the roles of individual birds are not fixed but may change flexibly between rounds, e.g. from being the producer/cacher at one occasion to being the scrounger/pilferer at another occasion (Bugnyar and Kotrschal, 2002a,b).

Clearly, pilfering imposes high costs to cachers, as any benefit of storing food is diminished when the food gets stolen before it can be recovered (Andersson and Krebs, 1978). One possibility to deal with the problem is to reduce the risk of being observed (see Vander Waal and Jenkins, 2003, for alternatives). Indeed, ravens engage in several countertactics such as increasing the distance to conspecifics for caching and using obstacles to hide from view (Bugnyar and Kotrschal, 2002b). They may also come back and quickly retrieve and/or aggressively defend their caches when other ravens come close. Interestingly, they do this specifically with birds that have been in the vicinity at the time of caching, whereas they refrain from going back to their caches with birds that came later (Bugnyar and Heinrich, 2005). Similar skills have been described for Western scrub jays Aphelocoma californica (Emery and Clayton, 2001; Dally, Emery and Clayton, 2005, 2006) and Clark’s nutcrackers Nucifraga columbiana (Clary and Kelly, 2011), indicating that paying attention to others at caching is key for proper cache protection in corvids.

Interestingly, similar arguments can be put forward for birds acting as pilferers. On one hand, ravens behave inconspicuously: they observe others caching from a distance and wait for the cachers to leave before they start a pilfering attempt (Bugnyar and Kotrschal, 2002b). This suggests that they are capable of controlling their intention and do not approach the desired food immediately, effectively avoiding any cache defense by the storer. On the other hand, they may also pay attention to other ravens in their vicinity and adjust their timing of pilfering depending on whether or not those potential competitors were present at caching. Specifically, they quickly attempt pilfering when others were around before but refrain from approaching the cache when others came later (Bugnyar and Heinrich, 2005). Finally, if caught in act by the cacher or a (dominant) competitor, pilferers may engage in displacement behaviors like digging in the soil or manipulating objects (Heinrich, 1999; Bugnyar and Heinrich, 2006), which may function to lead others away from the actual cache (Bugnyar and Kotrschal, 2004). To my knowledge, surprisingly little is known about the behavior of pilferers in corvids other than ravens. Aside of some anecdotal reports (e.g. Clayton & Emery, 2004; Källander, 2007), I am not aware of any experimental studies.

Does competition over hidden food select for advanced cognition? Under naturalistic set-ups, both cachers and pilferers engage in behavioral maneuvers that function to deceive others, i.e. they conceal information (e.g. by hiding outside view) and provide false information (e.g. by distracting others from the cache location). Cognitively, such deceptive maneuvers may be based on advanced skills like episodic-like memory for particular individuals (‘who has been around when and where’; Dally, Emery and Clayton, 2006) and, possibly, even perspective taking and knowledge attribution (‘that particular individual has or has not seen the cache and thus is knowledgeable or ignorant about its location’; Bugnyar and Heinrich, 2005; Clayton, Dally and Emery, 2007). Alternatively, these behaviors could be interpreted as orienting on behavioral cues and/or following a combination of learned rules (‘that particular individual was present at time of caching and thus likely pilfers caches’; e.g. Penn and Povinelli, 2007).

In the last few years, I conducted a series of experiments to distinguish between these interpretations. Specifically, I focused on the ravens’ knowledge about others when they act as pilferers (Table 1). This allowed me to use a human experimenter as cacher and thereby effectively control variables such as the number and location of caches and the exact use of cover material in the experiments (Table 2). Test subjects were always bystanders at two caching events which, after some delay, were confronted with competitors with the same, less, or no information about the caches (achieved by being also a bystander at both caches, one of the caches, or none of the caches; Figure 1a,b). Results revealed that birds could instantly (first trial) differentiate between competitors: they quickly approached either of the caches when confronted with conspecifics that were informed about both locations, whereas they selectively chose which cache to pilfer first when confronted with competitors that could see only one of the caches being made. Note that the focal subject always got a head start and the cover material was always placed in a way that all but the focal subject could see the food in the caches at the time of testing (Figure 1a,b), rendering the possibility of behavioral or emotional cueing from competitors unlikely (Table 2). These findings support the hypothesis that ravens can remember who was visually present at which caching events and that they can relate the presence at caching with a high risk of subsequent pilfering (Bugnyar, 2011).

In a next step, I tried to disentangle whether ravens base their pilfer decisions on their own perspective or, to some extent, also on the other’s perspective. Now, bystanders were trained to sit on a perch behind an opaque curtain that was either intact or partially intact, i.e. a window was cut out in the upper part at the height of the perch. In the intact condition, the curtain prevented the subject from watching the caching but also other bystanders from seeing their competitor; in the partially intact condition, the curtain still prevented the subject from seeing the caches being made but the view of other bystanders was not affected, i.e. they could see the competitor (Figure 2a). In a third condition, the curtain was pulled up so that the subject could watch the caching and bystanders could see the competitor. As in the previous experiment, two caches were made by a human experimenter and the tested raven got a head start in pilfering the caches in the presence of one of two possible competitors, observer of cache 1 or observer of cache 2 (Table 1, 2). Note that the curtain was always pulled up and competitor positioned on the ground during testing (Figure 2b). As in the previous study, the ravens instantly (first trial) matched the caches to the competitors, i.e. they pilfered cache 1 with observer of cache 1 and cache 2 with observer of cache 2. Interestingly, they did so only when the curtain was up at caching, i.e. the other raven could actually see the cache being made. In the partially intact curtain condition, in contrast, they did not show any preference for pilfering a particular cache first, performing similar to the intact curtain condition. Critically, in the partially intact condition the test subject could see the competitor at caching but the competitor’s view towards the cache was blocked. This supports the assumption that ravens base their choices not only on the memory of their own perspective (‘whom they could see at caching’) but they seemingly also take aspects of the other’s perspective into account (‘what the other could see at caching’).

Although the result of the last experiment is in line with the argument that maneuvers at food caching/pilfering can be explained with sophisticated cognition, it does not require full attribution skills, i.e. a Theory of Mind in the human sense (compare Povinelli and Vonk, 2003; Penn and Povinelli, 2007). For instance, the birds could have tracked the other’s line of sight behind the curtain and, on this basis, inferred their ‘knowledge state’ and risk of pilfering (Bugnyar, 2011). Indeed, experiments on gaze following showed that ravens are skilled in following other’s line of sight behind visual barriers (Bugnyar, Stöwe and Heinrich, 2004; Schloegl, Kotrschal and Bugnyar, 2007). Even more parsimonious would be the explanation that birds could have used subtle behavioral cues like head or body orientation to judge the other’s subsequent behavior. Although we attempted to control for such cues as much as possible at testing (i.e. by making both food pieces visible to the competitor; Table 2), this interpretation cannot be fully ruled out for the observation phase at caching. However, we explicitly tested for the use of gaze direction (defined as head and eye orientation) in another series of experiments involving the same individuals. Results of those studies were clear-cut: ravens naïve about a cache location were not able to learn to use gaze cues given by a conspecific and a human experimenter, respectively, in > 100 trials (Schloegl, Kotrschal and Bugnyar, 2008).

Finally, the ravens’ excellent performance in the pilfer setup cannot be explained by simple emotional and behavioral rules, which have recently been used successfully to model some cache protection skills (van der Vaart, Verbrugge and Hemelrijk, 2012). So far, these models cannot distinguish between different observers (‘who has watched when’). Moreover, they are based on the assumption that recovery rates of cachers are mediated by their arousal state, which in turn is affected by being observed. Unfortunately, nothing is known about the arousal state of bystanders that simply watch other bystanders at caching as was the case in the current experiments focusing on pilferers.

Taken together, ravens instantly differentiate between competitors with different knowledge states about food caches in several experiments. They likely remember who was present at which caching event and choose their behavioral strategies accordingly. Moreover, pilferers seem to be able to judge the other’s perspective by taking details of the other’s visual behavior into account, like its line of sight and its position relative to optical barriers. These findings support the assumption that maneuvers seen at pilfering are based on advanced socio-cognitive skills and may represent the first building blocks of abilities that go together with a ‘Theory of Mind’ in humans (Premack and Woodruff, 1978). Finally, our findings fit with recent results from Western scrub jays (Dally, Emery and Clayton, 2006) and chimpanzees Pan troglodytes (Hare, Call, Agnetta and Tomasello, 2000; Hare, Call and Tomasello, 2001), supporting the assumption that similar socio-cognitive skills have evolved convergently in some mammals and birds (Emery and Clayton, 2004). Still, we have to bear in mind that none of these studies provide unequivocal evidence for mental attribution (Penn and Povinelli, 2007; Shettleworth, 2010). The current studies on ravens are one of the first to reject the possibility that individuals merely associate what they have seen (i.e. the presence/absence of given individuals) with a high or low chance of competitively retrieving food (see also Bräuer, Call and Tomasello, 2007). Yet, there is much room for interpretation (Lurz, 2011) and further studies are needed to clarify which cognitive mechanisms non-human animals use for differentiating between ‘knowers’ and ‘guessers’.

How do ravens acquire their sophisticated knowledge about others at caching/pilfering? Given the numerous variables ravens have to consider for appropriate caching and successful pilfering, it not surprising to find strong evidence for learning, particularly during the first year of life (Bugnyar, Stöwe and Heinrich, 2007). However, making mistakes with food is costly: specifically when parents reduce the provisioning, young birds that loose their cached food to others may remain hungry. Interestingly, up to half of the caches made in the first months of life contain nonedible items only. These object caches are camouflaged in a similar way as food caches and readily elicit pilfer responses of other ravens (Bugnyar, Stöwe and Heinrich, 2007). Contrary to food caching, birds do not improve in object caching over time. Instead, they continue to show a high variation in when they make object caches and where they place them. For instance, birds could wait for a conspecific to approach and place an object cache directly in front of it or they could leave with an object and cache it far away/outside view from the other, as if to test the other’s response. Ravens also show this variation with unfamiliar human experimenters providing the birds with colored small objects, particularly during their first encounters (Bugnyar, Schwab, Schloegl, Kotrschal and Heinrich, 2007). When these human experimenters were instructed to behave towards the object caches in one of two ways, i.e. pilfer or just look at them, the ravens quickly learned to differentiate between ‘efficient’ and ‘nonefficient’ human pilferers of object caches. Importantly, they used this information only when caching food, i.e. they protected their food caches from ‘efficient’ pilferers but not from ‘non-efficient’ ones (Bugnyar, Schwab, Schloegl, Kotrschal and Heinrich, 2007). Playful caching of objects may thus represent a way of acquiring information about others, i.e. their behaviors at caching and/or pilfering, without the risk of losing precious food.

When taking a broad look at the ontogeny of cache protection and pilfering skills, two further points become apparent: first, it takes ravens a relatively long time to show full flexibility in their maneuvers, i.e. in appropriately using the learned information for deceiving others. Moreover, clear signs of concealment (hiding from view) come almost at same age in all subjects, hinting towards a developmental step (Bugnyar, Stöwe and Heinrich, 2007), possibly for controlling intentions. A similar age effect was found in the development of geometrical gaze following (Schloegl, Kotrschal and Bugnyar, 2007), indicating that the ability of tracking the other’s line of sight develops in close continuity with the ability of hiding from view. Secondly, ravens continue to play caching/pilfering with objects for years (Bugnyar, Schwab, Schloegl, Kotrschal and Heinrich, 2007). Possibly life in non-breeder groups with a high degree of fission-fusion dynamics require them to repeatedly learn about unfamiliar individuals and/or update information about hardly familiar individuals. In any case, it may indicate that ravens build up knowledge about others, often over years. The question then is how much they can use this information in daily life situations other than competition for caches?

Life in non-breeder groups: association patterns and social bonds

Anonymous aggregations or individualized societies? Ravens assembling at large food sources, like the moose carcass in the example above, could represent an anonymous crowd (Heinrich, 1988). However, birds of a given area regularly meet at nocturnal roosts, which they may use as information centers (Marzluff, Heinrich and Marzluff, 1996; Wright, Stone and Brown, 2003). Thus, it could be that (at least some) birds are quite familiar to one another. Preliminary support for this assumption comes from recent findings of subgroups with different foraging strategies (Dall and Wright, 2009), whereby ravens using the same roost tend to search for food together (Wright, Stone and Brown, 2003; but see Heinrich, Kaye, Knight and Schaumburg, 1994).

At our own study site in the Austrian Alps, we even find temporarily stable elements in non-breeder groups (Braun, Walsdorff, Fraser and Bugnyar, 2012; Braun and Bugnyar, in press). About one third of 200 individually-marked ravens develop a preference for particular foraging areas, using them almost on a daily basis. The majority of the marked population visits these areas from time to time, spending a few days up to months in the valley around the foraging sites. Together, this results in a moderate fluctuation in the composition of the foraging groups across months. Still, the non-breeders show a high degree of fission-fusion dynamics over the day, forming large groups for foraging and roosting but small groups of different composition for the rest of the day (Braun, Walsdorff, Fraser and Bugnyar, 2012).

Specifically in these small groups, ravens engage in a range of socio-positive behaviors. They may share and offer food or, in a playful manner, show and offer non-food items (Pika and Bugnyar, 2011); they may sit in close contact (within reach of the other’s beak) and engage in reciprocal allopreening (analogue to grooming in primates) with particular individuals over extended time periods (Figure 3). This fits with reports from captive colonies (Lorenz, 1935; Gwinner, 1964; Heinrich, 1999) and suggests that ravens form affiliate relationships or social bonds. Importantly, such social bonds are not restricted to reproduction (mated pairs) but can be found in all age classes (juveniles in their first year, subadult and adults), between and within sexes and, to some extent, also between siblings of the same clutch (Fraser and Bugnyar, 2010a; Loretto, Fraser and Bugnyar, 2012; Braun and Bugnyar, in press).

Role of social bonds.

Having affiliates or bonding partners may pay off in various ways. Notably, it increases the individuals’ access to food. Under field conditions, this is true for both partners, irrespective of their sex and age class (Braun and Bugnyar, in press). In experiments with captive birds, the presence of bonded individuals (siblings, friends) affects the time spent in exploring new objects (Stöwe et al., 2006), the amount of attention paid to others (Scheid, Range and Bugnyar, 2007) and the likelihood of social learning (Schwab, Bugnyar, Schloegl and Kotrschal, 2008). Interestingly, ravens do not have many bonds simultaneously but focus on one up to a few partners at a time. However, bonding intensity and partners may change across seasons and years (Braun and Bugnyar, in press). Birds spending several years in non-breeder groups may thus end up with a decent number of affiliated individuals. Recent studies on captive ravens indicate excellent memory not only for former group members but also for the relationship valence they had with those individuals. Birds that were kept in one social group for two years responded differently to playbacks of calls from former affiliates and non-affiliates three years later (Boeckle and Bugnyar, 2012).

How comparable are avian social bonds to the primate/mammalian concept of bonds? Corvid social bonds reflect the content, quality and pattern of interactions over time (Emery, Seed, von Bayern and Clayton, 2007), fitting Hinde’s (1976) definition of social relationship. Following the theoretical framework of Cords and Aureli (2000), individual variation in relationship quality may be characterized by three main components: value, compatibility, and security. Interestingly, we found three main components describing the relationship quality of ravens (Fraser and Bugnyar, 2010a). Notably, these components were almost identical to those found in chimpanzees (Fraser, Stahl and Aureli, 2008) and, accordingly, they were labeled as value, compatibility and security. Kinship and sex combination had different effects on the components: kin relations were of high value, whereas male-male and male-female relations were more stable and secure than female-female relations, at least in this group of captive birds (Fraser and Bugnyar, 2010a).

As in primates (Fraser, Stahl and Aureli, 2008; Fraser, Koski, Wittig and Aureli, 2009), the components of relationship quality are a good predictor for the ravens’ behavior during and after conflicts (Fraser and Bugnyar, 2010b, 2011, 2012). Birds may provide help in ongoing conflicts by joining others in fights and/or chases, whereby they tend to support their kin and those who preened them and helped them before (Fraser and Bugnyar, 2012). Ravens may also show post-conflict behavior when they were engaged in the conflict themselves but also when they were just bystanders.

Reconciliation between former opponents is expected to occur when those individuals share a valuable relationship Aureli and de Waal, 2000). Ravens do show this pattern (Fraser and Bugnyar, 2011) but not in all studies (Fraser and Bugnyar, 2010b), probably due to the rare occurrence of conflicts between close affiliates. Much more often than reconciliation, bystander affiliation can be observed. Notably, if victims of aggression share a valuable relationship with one of the bystanders, they may seek its affiliation or actively get approached by that bystander for affiliate contacts (Fraser and Bugnyar, 2010b; see Seed, Clayton and Emery, 2007 for a similar findings in rooks, Corvus frugilegus). By initiating bystander affiliation, victims may try to protect themselves from any renewed aggression. In contrast, when a third party initiates affiliation, this usually follows a severe conflict (Fraser and Bugnyar, 2010b). Importantly, in those cases the value of the relationship between the bystander and the victim of aggression is higher than the value of the relationship between the bystander and the aggressor, indicating that the affiliate behavior towards the victim may serve as consolation rather than as a form of mediated reconciliation compare Wittig, Crockford, Wikberg, Seyfarth, and Cheney, 2007; Fraser, Koski, Wittig and Aureli, 2009). Furthermore, we found no signs of incompatibility or insecurity in the relationship between the bystander and the victim of aggression, suggesting that there is no need for the bystander to protect itself from redirection of conflicts by the victim (compare Koski and Sterck, 2007).

Synopsis

Recent research on wild and captive ravens allows us to identify two lines of potential driving forces for evolution of cognitive skills in this bird species, (i) competition over food caches and (ii) forming and maintaining social bonds. The former may go together with improved inhibition skills (control of intentions) and the deceptive use of social knowledge e.g. when/from whom to hide). Moreover, competition for hidden food likely results in elements of perspective taking (in the sense of understanding visual barriers, projecting other’s line of site) and, possibly, attribution skills (in the sense of remembering who could and could not see the caching). Although our understanding of the social structure of avian groups with high degrees of fission-fusion dynamics and seasonal patterns in group composition is still rudimentary, it appears safe to say that raven non-breeder groups are structured by different forms of social relationships.

Notably, birds form social bonds not only for reproduction; they can be found in several age classes, probably because bonded individuals serve as cooperation partners for gaining status and/or access to resources. The quality of raven social relationships resembles that reported for chimpanzees and, similar to mammals, the value of their bonds becomes apparent in alliance formation and conflict management. Hence, similar socio-cognitive skills may evolve independently of phylogeny in systems with a given degree of social complexity.

Relating these findings back to the arguments raised in the introduction, we may end up with the problem of how to classify species like ravens in the social spectrum? This question seems critical for any comparisons involving highly mobile species like birds that may spend time in smaller and larger groups depending on season and/or their stage in life history. Clearly, using breeding system (e.g. Iwaniuk and Arnold, 2004) or mean foraging group size (e.g. Emery, 2006) as proxi for complexity does not cover the full picture.

Taking aspects of relationship quality such as a long-term pair bond into account (e.g. Dunbar and Shultz, 2007; Emery, Seed, von Bayern and Clayton, 2007) has been a step forward; understanding how many individuals are dealt with on a personal basis and what type of relationships are important in what context and/or period of life seem to be the next logical steps. Characterizing the ‘complexity’ of social systems in more than one dimension may thus be a possible solution to our problem.

Research on social cognition has already gained much from broadening the focus to species outside of primates (e.g. McComb, Moss, Sayialel and Baker, 2000; Holekamp, Sakai and Lundrigan, 2007), as this allows testing assumptions independently of phylogeny. The value of including an even broader range of species into the picture, notably those classified as moderately social, is that it may allow us to refine our current models and may help to specify what aspects of social life select for which type of socio-cognitive skills.

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How to Reference This Article:

Bugnyar, T. (2013). Social cognition in ravens. Comparative Cognition & Behavior Reviews, 8, 1-12. Retrieved from https://comparative-cognition-and-behavior-reviews.org/index.html doi:10.3819/ccbr.2013.80001

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Cerebral and behavioural asymmetries in animal social recognition

by Orsola Rosa Salva,
University of Trento, Italy

Lucia Regolin,
University of Padova, Italy

Elena Mascalzoni,
University of Padova, Italy

Giorgio Vallortigara,
University of Trento, Italy

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Abstract

Evidence is here summarized that animal species belonging to distant taxa show forms of social recognition, a sophisticated cognitive ability adaptive in most social interactions. The paper then proceeds to review evidence of functional lateralization for this cognitive ability. The main focus of this review is evidence obtained in domestic chickens, the animal model employed in the authors’ laboratories, but we also discuss comparisons with data from species ranging from fishes, amphibians and reptiles, to other birds and mammals. A consistent pattern emerges, pointing toward a right hemisphere dominance, in particular for discrimination of social companions and individual (or familiarity-based) recognition, whereas the left hemisphere could be specialized for “category-based” distinctions (e.g., conspecifics versus heterospecifics). This pattern of results is discussed in relation to a more general specialization and processing styles of the two sides of the brain, with the right hemisphere predisposed for developing a detailed, global and contextual representation of objects, and the left hemisphere predisposed for rapid assignment of a stimulus to a category, for processing releaser stimuli and for control of responses.

Keywords: social recognition, individual recognition, lateralization, comparative studies

Salva, O.R., Regolin, L., Mascalzoni, E., & Vallortigara, G. (2012). Cerebral and behavioural asymmetries in animal social recognition. Comparative Cognition & Behavior Reviews, 7, 110-138. Retrieved from https://comparative-cognition-and-behavior-reviews.org/ doi:10.3819/ccbr.2012.70006

Information Seeking in Animals: Metacognition?

by William A. Roberts,
University of Western Ontario

Neil McMillan,
University of Western Ontario

Evanya Musolino,
University of Western Ontario

Mark Cole,
University of Western Ontario

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Abstract

Metacognition refers to humans’ ability to monitor the state of their own learning and to judge the correctness of information retrieved from memory. Inferences about metacognition-like processes in non-human animals have been made from studies in which subjects judge the adequacy of previously presented information and from information seeking studies in which no prior knowledge exists. This article briefly reviews the former type of experiments but focuses on studies of information seeking. A number of studies now indicate that apes and monkeys will look down opaque tubes or under opaque containers to see the location of a hidden reward. They less often make looking responses when other information indicates the location of reward, such as visible baiting, transparent tubes or containers, or logical inference. Studies of information seeking in pigeons, rats, and dogs are reported that indicate they do not readily show the types of looking responses seen in primates. If given a forced choice between stimuli that do and do not yield information about the location of reward, however, these non-primates make the informative choice. It is suggested that the choice of information in these pigeon, rat, and dog experiments may be a form of secondary sign-tracking and thus different from the metacognition-like processes used by primates.

Keywords: comparative cognition, information seeking, metacognition, observing response, sign tracking

Roberts, W. A., McMillan, N., Musolino, E., & Cole, M. (2012). Information Seeking in Animals: Metacognition? Comparative Cognition & Behavior Reviews, 7, 85-109. Retrieved from https://comparative-cognition-and-behavior-reviews.org/ doi:10.3819/ccbr.2012.70005

The Predictably Unpredictable Operant

by Allen Neuringer
Reed College

Greg Jensen
Columbia University

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Abstract

Animals can learn to repeat a response when reinforcement is contingent upon accurate repetitions or to vary when reinforcement is contingent upon variability. In the first case, individual responses can readily be predicted; in the latter, prediction may be difficult or impossible. Particular levels of variability or (un)predictability can be reinforced, including responses that approximate a random model. Variability is an operant dimension of behavior, controlled by reinforcers, much like response force, frequency, location, and topography. As with these others, contingencies of reinforcement and discriminative stimuli exert precise control. Reinforced variability imparts functionality in many situations, such as when individuals learn new responses, attempt to solve problems, or engage in creative work. Perhaps most importantly, reinforced variability helps to explain the voluntary nature of all operant behaviors.

Keywords: operant variability, voluntary, determinism, random, choice

Neuringer, A., & Jensen, G. (2012). The Predictably Unpredictable Operant.Comparative Cognition & Behavior Reviews, 7, 55-84. Retrieved from https://comparative-cognition-and-behavior-reviews.org/ doi:10.3819/ccbr.2012.70004