Bridging levels: Using a cognitive model to connect brain and behavior in category learning

Bridging Levels: Using a Cognitive Model to Connect Brain and Behavior in Category Learning Todd M. Gureckis (tgurecki@indiana.edu) Department of Psychology, Indiana University 1101 E. 10th Street, Bloomington, IN 47405 USA Bradley C. Love (love@psy.utexas.edu) Consortium for Cognition and Computation, University of Texas at Austin Austin, TX 78712 USA Abstract to predict how degraded function along this circuit af- fects category learning performance for these (and other) groups. In particular, SUSTAIN relates the degree of preserved function to how readily members of a group can individuate events, as opposed to collapsing experi- ences together into a common gestalt (see Figure 1). After introducing the model, we explain the close cor- respondence between aspects of the model and the cur- rently understood function of a learning circuit involving PFC, the hippocampus, and perirhinal cortex. We then review a number of simulations which support our the- ory. In doing so, we provide a novel framework for under- standing the role this circuit plays in category learning ability. In addition, our analysis suggests a recasting of several dichotomies popular in the field, such as the distinction between categorization and recognition, rec- ollective and familiarity-driven responding, and episodic and semantic memory. Mental localization efforts tend to stress the where more than the what. We argue that the proper targets for lo- calization are well-specified cognitive models. We make this case by relating an existing cognitive model of cat- egory learning to a learning circuit involving the hip- pocampus, perirhinal, and prefrontal cortex. Results from groups varying in function along this circuit (e.g., infants, amnesics, older adults) are successfully simu- lated by reducing the model’s ability to form new clus- ters in response to surprising events, such as an error in supervised learning or an unfamiliar stimulus in un- supervised learning. Reported task dissociations (e.g., categorization vs. recognition) are explained in terms of cluster recruitment demands. A major goal of cognitive psychology has been to de- velop an understanding of behavior in terms of compu- tational principles. However, we are often left with the question of what these models tell us about the brain. The answer is certainly not clear. The growing area of cognitive neuroscience offers an endless source of new embers for this debate, as more and more cognitive func- tion is localized and described in terms of specific brain processes. However, by focusing on the localization of mental function (i.e., where is processes X in the brain? ), we run the risk of amassing a list of brain areas associ- ated with certain tasks in the absence of useful linking theories reflecting how those regions interact to control behavior in our daily lives. In this paper, we argue that well-specified, process models of cognitive functions are the appropriate targets for localization. Successful process models offer a num- ber advantages over folk psychological, ad hoc, or tra- ditional psychological theories. For example, model de- veloped in cognitive psychology make predictions, have mechanisms and dynamics which can be related to brain measures, and offer a simple and clear starting point for developing theories of brain function. To support our conjecture, we focus on relating a process model of hu- man category learning to a learning circuit involving the hippocampus, perirhinal cortext, and prefrontal cortex (PFC). The model we consider, Supervised and Unsupervised STratified Adaptive Incremental Network (SUSTAIN), is applied to human data from a number of populations (infants, amnesics, and older adults) who differ in their category learning ability. Armed with its computational principles and the proposed mapping, SUSTAIN is able SUSTAIN and the Proposed Mapping We begin by introducing the computational theory (SUSTAIN) and the bridge theory linking SUSTAIN to functional components in the brain. Due to lim- ited space, readers interested in the mathematical de- tails of the model are directed elsewhere (Love, Medin, & Gureckis, 2004). SUSTAIN The basis for representing category knowledge has been proposed to be rule-based, exemplar-based, or prototype-based. SUSTAIN proposes that clusters, which display characteristics of all three of the afore- mentioned approaches, underlie our category represen- tations. A cluster is a bundle of features that captures conjunctive relationships across dimensions. For exam- ple, a cluster can capture the fact that having wings, flying, and having feathers tend to co-occur. In SUSTAIN, categories are represented by one or more clusters. For example, the category of birds might be represented by multiple clusters which capture natu- ral patterns of regularity within the category (e.g., song birds, birds of prey, penguins, and ostriches each might be represented by separate clusters which all belong to the super-ordinate). A cluster can also belong to mul- tiple categories at once because they are linked to cat- egories by association weights that are adjusted during learning.