The Future Is Computational Comparative Cognition
Abstract
Computational modeling should and will play an increasingly important role in the future of comparative cognition. Computational comparative cognition is a burgeoning field, poised to tackle perennial questions about animal behavior as well as to take the opportunity to ask new ones. To establish computational comparative cognition as a field, researchers must work together to create interdisciplinary collaborations, to translate and disseminate findings that can be digested by a diverse audience, and to rethink the status of computational modeling. Blending models, experiment, and observation will deepen our understanding of animal behavior, promising a bright future for the field of comparative cognition.
Keywords: computational modeling, machine learning, artificial intelligence, interdisciplinarity
Of 319 participants in a recent survey of animal behavior researchers, 75 (23.51%) reported using computational modeling in their research (Voudouris, Cheke, et al., 2023).1 We predict that future comparative cognitive scientists will use computational techniques to a much greater extent than today. Computational modeling is a new frontier, providing the tools we will need to settle old debates and answer perennial questions about nonhuman animal cognition. Although such methods have been implemented in associative learning theory, neuroethology, and computational neuroscience for several years, many open research questions remain about how to model, for example, episodic memory, theory of mind, communication, physical reasoning, and metacognition in nonhuman animals. We argue that this demands interdisciplinary collaboration with the computational cognitive sciences. Successful collaboration of this kind in turn requires, first, an infrastructure for conducting computational comparative cognition, including a shared vocabulary and a common methodology; and second, an openness to formal methods for studying nonhuman animal cognition.
The computational cognitive sciences, including artificial intelligence (AI) and machine learning, offer an exciting toolbox for understanding behavior. These methods can give comparative cognitive scientists the opportunity to bring new hypotheses and theories to the table; build formal, precisely stated theories of cognition that generate testable predictions; and a chance to understand the mechanisms underlying behavior. Rich computational models have already made waves in the study of human cognition. Pouncy and Gershman (2022) synthesised the theories of reinforcement learning in computer science with theories about induction and decision making from cognitive psychology (Griffiths et al., 2010; Lake et al., 2017) to build sophisticated models of human learning and decision making. The nascent research field of predictive processing (Friston, 2010) seeks to model how humans make predictions about their environment and behave accordingly, making use of techniques from Bayesian inference and causal modeling. The methods of causal inference (Gopnik et al., 2004; Gopnik & Tenenbaum, 2007; Pearl, 2009) and probabilistic programming (Lake et al., 2015; Ullman & Tenenbaum, 2020) have revolutionized how we think about human behavior and cognition. We think that this computational revolution should extend to comparative cognition, augmenting traditional experimental and ethological approaches with contemporary AI and machine-learning techniques (Griffiths, 2015).
In recent years, we have seen several examples of the use of computational models to offer testable novel explanations of animal behavior. For example, Elske van der Vaart and colleagues (2012) used a simulation to present a novel hypothesis about the caching behavior of scrub jays. When scrub jays are in the presence of conspecifics, they will either avoid caching food or cache it and then return later to move it to another location in private. One hypothesis about why they do this is that they are able to attribute mental states to conspecifics, reasoning that if they are present, they will remember the location of the cached food and intend to pilfer it at a later time. Another hypothesis is that the birds have simply learned to associate the presence of conspecifics with an increased likelihood of pilfering, without any attribution of mental states. Van der Vaart et al. suggest an alternative account, in which the presence of conspecifics increases stress, which in turn increases the frequency of (re-)caching. They built a simulation of a caching bird in an environment with a variety of onlookers and demonstrated how the behavior of the simulated bird matches the behavior of real birds in the laboratory. The result is a novel hypothesis about caching behavior that generates precise and testable predictions for future study.
More recently, Johanni Brea and colleagues (2023) built computational models of episodic-like memory of caching birds, including Clark’s nutcrackers and Eurasian jays. They tackled the important question of whether caching behavior, which requires the bird to remember what it stored where and when, implies that these birds can simulate past experiences (i.e., mental time travel). They built computational simulations to contrast this “mental replay” hypothesis with the idea that caching can be explained through associating spatiotemporal cues with motivational states, such as hunger (“plastic caching”). The plastic caching model flexibly encodes what the bird cached, where, and when. However, this type of what–where–when memory does not imply an ability for mental time travel in the same way as mental replay. To test these models, Brea and colleagues translated laboratory experiments into a formal schema that a neural network could interpret. They found that both models matched the behavior of real birds in the laboratory. These cases illustrate how researchers can use computational modeling to generate new hypotheses explaining behavior. Such approaches allow one to explore the space of plausible hypotheses more widely than traditional experimental methods.
Computational methods provide additional opportunities for understanding the mechanisms underlying sophisticated nonhuman animal behavior. Manuel Baum and colleagues (2022) proposed yoking as an important tool for doing this. In their yoking procedure, a computational model of behavior is designed to receive the same inputs and perform the same actions as an animal in the laboratory. Baum and colleagues used yoking to study how Goffin’s cockatoos learn to solve a complex physical problem. In their study, cockatoos were presented with a puzzle box containing a reward. To obtain the reward, participants needed to learn a specific sequence of actions: Opening the door to the box requires removing a metal bar, which requires removing a metal disc. Baum and colleagues then provided the states of the puzzle box and actions of the cockatoo as inputs into a reinforcement learning algorithm. Finally, they compared the performances of different algorithms to the learning trajectories of the cockatoo. Using this approach, the researchers were able to identify plausible cognitive mechanisms underlying problem-solving behavior in cockatoos with more precision than methods that rely on behavioral experiments alone.
Computational methods have a lot to offer comparative cognition. However, their integration into contemporary research might require a change in how we think about such models. Computational modeling and simulation are central to some areas of comparative cognition, such as those focused on associative learning. Researchers have made significant progress towards building formal models for how animals learn to relate stimuli with responses (Mackintosh, 1983; Rescorla, 1988; Rescorla & Wagner, 1972), as well as extending those accounts to explain phenomena such as apparent future planning (Lind, 2018), tool-use (Taylor et al., 2010, 2012), and other sophisticated behaviors (Cardoso et al., 2023; Lind & Vinken, 2021). However, in many areas of comparative cognition, where the focus has been the study of humanlike behavioral phenomena in nonhuman animals, there has been a tendency to view these models as too simplistic.
This tendency emerges in two main ways. Sometimes, the computational gymnastics that need to be performed to render a complex physical experiment into something that can be run as a simulation on a computer can eliminate many of the interesting complexities of the task. Take the yoking study of cockatoos. To characterize the puzzle box as a reinforcement learning problem, complex action sequences are abstracted to simple representational units. The action of “remove the metal disc” goes from being a complex collaboration between the bird’s beak and claws to a single binary variable. From there, the argument can be made that many of the complexities of the task for the bird have been simplified into oblivion by computational modeling, so its explanatory relevance is severely limited (Lind, 2018; Lind & Vinken, 2021). Second, mathematical models of cognition have often been viewed as necessarily simpler than equivalent cognitive theories. Take the classic dichotomies between associative learning and cognitive processes such as theory of mind (Penn & Povinelli, 2007), metacognition (Smith et al., 2014), and social learning (Papineau & Heyes, 2006). In many of these cases, the more computational, formalized hypotheses are taken to be simpler. Because of their putative simplicity, they are sometimes taken as necessarily incapable of capturing the flexibility and sophistication of non-human animal behavior (Buckner, 2011; Heyes, 2012).
Models and simulations of physical phenomena will almost always involve idealization and abstraction to some degree (Potochnik, 2017; Weisberg, 2012). This does not undermine their utility in revealing new insights about the world, when used in conjunction with other scientific methodologies, such as laboratory experiment and behavioral observation. Indeed, it is through a plurality of approaches that we arrive at robust answers about animal cognition (see Heesen et al., 2019). Moreover, that computational models are formal does not imply that they are simpler than other models in a pernicious sense. Simplicity comes in many forms (Dacey, 2016; Meketa, 2014; Starzak, 2017). Commonsense explanations, for example, are simpler for many of us to understand, but as C. Lloyd Morgan (1894, 1903) emphasized, this does not mean that we should prefer such accounts (Fitzpatrick, 2008). To fully embrace the benefits of computational modeling and simulation in comparative cognition, we may need to change how we think about these models. We believe a useful starting point involves spelling out the ways in which such models simplify the target system under investigation and assessing the epistemic benefits and obstacles that arise from such simplifications.
A computational future for comparative cognition looks bright, but it requires the synthesis of insights from multiple disparate fields. This interdisciplinary endeavour will pose challenges, such as philosophical and methodological divergences in terms of the stated role of computational modeling and a significant language barrier. Take, for example, the notion of instrumental learning in comparative cognition and contrast it to reinforcement learning in computer science. At a high level, these fields appear to be tackling the same problem: how to model the association between stimulus, action, and reward. However, apparent incommensurability emerges in the details, and efforts to translate between the fields are difficult (Sutton & Barto, 2018). We think that this sort of interdisciplinary integration will take time. To encourage and accelerate this process, comparative cognition must pursue expressly interdisciplinary projects. A fantastic example of how computer scientists, engineers, biologists, and cognitive scientists can work together is the Earth Species Project. This organization’s aim is to use the tools of modern machine learning and AI to improve our understanding of nonhuman animal communication (see, e.g., Hagiwara et al., 2022, 2023; Hoffman et al., 2023; Rutz et al., 2023). Similarly, the Major Transitions Project seeks to unite computational biologists, philosophers, and cognitive scientists to understand the evolution of cognition (Barron et al., 2023). In an effort to build a shared infrastructure for research across computer science, computational neuroscience, and psychology, the Leverhulme Centre for the Future of Intelligence at the University of Cambridge has developed the Animal-AI Environment (Beyret et al., 2019; Crosby et al., 2020; Voudouris, Alhas, et al., 2023). This research platform is for conducting cognitive experiments with artificial agents, humans, and nonhuman animals in a directly comparable, more ecologically valid manner (Voudouris et al., 2022). The Animal-AI Environment offers the opportunity for researchers from diverse fields to work side by side and collect computational and real-world laboratory data. As researchers work on similar problems on a common platform, the height of the language barrier can be slowly reduced, and these fields can cross-pollinate for mutual progress.
Computational comparative cognition is a burgeoning field, poised to tackle perennial questions about animal behavior, as well as to take the opportunity to ask new ones. To establish it as a field, we must work to create interdisciplinary collaborations, to translate and disseminate findings that can be digested by a diverse audience, and to rethink the status of computational modeling in comparative cognition. We are excited about an interdisciplinary future, blending models, experiment, and observation to deepen our understanding of animal behavior.
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