Learning where (not) to cache : A cognitive model for corvids

Learning Where (Not) To Cache: A Cognitive Model for Corvids Elske van der Vaart (e.e.van.der.vaart@rug.nl) 1,2 Charlotte Hemelrijk (c.k.hemelrijk@rug.nl) 1 Rineke Verbrugge (l.c.verbrugge@rug.nl) 2 Theoretical Biology, University of Groningen, P.O. Box 14 9750 AA Haren, the Netherlands Artificial Intelligence, University of Groningen, P.O. Box 407 9700 AK Groningen, the Netherlands Abstract Computational modeling of cognition has provided many new insights into the human mind. In this paper, we use the same technique to advance our understanding of other, nonhuman minds: Those of corvids. This family of birds stores food under ground, saving it for later. This process of caching and recovery has been used to study many different aspects of corvid cognition, making it possible to build one integrated model that can be used to study many different cognitive phenomena. We start the construction of such a model by focusing on memory, and validate it by replicating three experiments by de Kort et al. Here, the caches of scrub jays are systematically stolen or moved, and the question is how this will affect their choice of cache sites. We use our model to reproduce the empirical data, and confirm its robustness by demonstrating that there are alternative outcomes that it could not have fit. In the process, we provide a new perspective on what exactly scrub jays may be learning and remembering. Keywords: Computational model; cognition; corvid; scrub jay; Aphelocoma californica; caching; memory; learning. Introduction Computational models of cognition have significantly advanced our understanding of the human mind (Sun, 2008). Therefore, the same techniques hold promise for advancing our understanding of other, nonhuman minds. Essentially, what such models do, is to implement a theory of cognition as a working computer program, making it possible to very precisely test a theory’s assumptions, conclusions and predictions. In the field of comparative cognition, many questions are being asked about animal memory, learning and problem solving (Premack, 2007). Innovative experiments have produced a wealth of empirical data, but results can be difficult to interpret. Computational models offer one way of testing the plausibility of competing explanations (Penn, Holyoak, & Povinelli, 2008). Studies of jays, nutcrackers and other corvids - members of the extended family of crows - seem particularly likely to benefit from this approach. In terms of their cognitive abilities, corvids have been likened to apes: One species uses tools, another solves trap-tube problems, a third can infer hierarchical relationships (Emery & Clayton, 2004). But what makes corvids particularly attractive from a modeling perspective, is that some of the most interesting results have been obtained within a single experimental paradigm - that of caching and recovery. Virtually all corvids cache: They bury food items under ground, saving them for later. It can be days, weeks, or months before they return to eat them. This behavior has been extensively studied in the laboratory, and depends on memory. Corvids’ natural tendency to cache and recover has been used to study memory mechanisms, various kinds of learning, use of visual landmarks, future planning, and social cognition, among other things (de Kort et al., 2006). Usually, in these experiments, the birds are presented with a discrete set of cache sites to choose from, a number of visual landmarks, the presence or absence of a conspecific, and very little else. This means a uniform setup is being used to study a diverse set of cognitive phenomena, making it possible to validate a single computational model across a large variety of experimental results. In this paper, we start the design of such a model by focusing on memory, for cache and recovery events. To store these, we draw inspiration from the ACT-R cognitive architecture, and the way it uses chunks (Anderson, 2007). Here, a chunk is a declarative fact, stored in memory, with an activation that depends on its own history of use, as well that of related chunks. What this allows us to do, is to encode both a bird’s options and its memories as chunks, and to have the most active option chunk determine its next action. Then, if we let memory chunks affect the activations of option chunks, we can simulate how previous cache and recovery events affect future cache and recovery choices. We validate our cognitive model, and test its usefulness, by replicating three experiments by de Kort et al. (2007). This work features Western scrub jays, Aphelocoma californica, and looks at whether they learn to adjust their choice of cache sites in response to their experiences at recovery. In these experiments, a scrub jay in its cage is presented with a bowl of worms, and a number of visually distinct ice cube trays to cache in. After it has cached for fifteen minutes, all trays are taken away, only to be returned a day later, with all caches in one of the trays missing, or moved. This procedure is repeated across a number of trials, and the question is whether the scrub jay will change its caching behavior over time, and if it does, what the mechanism is. Is it responding to punishment, avoiding trays that are always pilfered, or is it responding to reward, preferring trays where it always finds its worms? Our computational model successfully reproduces the empirical data, and thereby gives a new answer to this question.