Mathematical Models of Visual Category Learning Enhance fMRI Data Analysis

Mathematical Models of Visual Category Learning Enhance fMRI Data Analysis Emi M Nomura (e-nomura@northwestern.edu) Department of Psychology, 2029 Sheridan Road Evanston, IL 60201 USA W Todd Maddox (maddox@psy.utexas.edu) Department of Psychology, 1 University Station Austin, TX 78712 USA Paul J Reber (preber@northwestern.edu) Department of Psychology, 2029 Sheridan Road Evanston, IL 60201 USA Abstract one learning system over another. However, this approach presupposes that learning the categories only recruits the operation of one system at a time. A key question is whether the systems are independent or competitive. Here we collected fMRI data from two groups performing visual category learning in two different ways. The underlying category structure of the to-be-learned categories differed between groups so as to encourage one type of strategy over another. Mathematical models of learning were fit to the behavioral data to identify the specific strategy each subject was using. Modeling identified particularly good instances of strategy use, but also demonstrated that some subjects used the sub-optimal strategy regardless of their experimental group. We then analyzed the fMRI data in two different ways. The first grouped subjects according to their experimental group, which assumed that the group assignment was sufficient to elicit the appropriate learning strategy. The second re- grouped the subjects according to their preferred strategy. By looking selectively at subjects who best exhibited each strategy, additional elements of the neural circuits supporting each type of category learning were identified. The results of these analyses demonstrate that models of category learning can effectively guide fMRI data analysis. Likewise, imaging can provide additional constraints to existing models of categorization. Models of categorization instantiate specific hypotheses about psychological processes controlling behavior. Neuroimaging provides a tool to visualize the neural correlates of these same processes in human subjects. By combining techniques, we can begin to make the connection between behavior and neural activity. Here we collected fMRI data using a category learning paradigm where subjects were encouraged toward a particular strategy by the underlying category structure. The application of mathematical models of category learning to this behavioral data enabled the organization of fMRI data according to the actual strategy employed. Keywords: categorization; modeling; neuroimaging Categorization is a process by which the brain assigns meaning to stimuli. Experience with these stimuli facilitates the grouping of distinct items that may share similar properties into categories, such as ‘cars’ or ‘birds. ’ This grouping is critical for rapidly and appropriately selecting behavioral responses. While there are numerous categories that we’ve already acquired over our lifetime, adults possess the ability to acquire new categories. It is this learning process that is under investigation here. The study of categorization has a long history in cognitive psychology, particularly in the domain of computational and mathematical modeling. A variety of models exist that support different theories about the cognitive operations involved in categorization. These models are traditionally tested by comparison to human behavioral data in an attempt to understand the specific operations that contribute to categorization. Collecting functional magnetic resonance imaging (fMRI) data while subjects are undergoing category learning allows us to test hypotheses about the involvement of neural processes in theoretical models of categorization. Recently, a number of different neuroimaging studies have investigated the neural correlates of category learning in different tasks. In general, the data suggests that there exist multiple neural systems that can support category learning. One approach aimed at dissociating these systems is to present different category structures that preferentially elicit Models of categorization In the literature, models designed to capture category learning behavior can be grouped into three general types: exemplar, prototype and decision-boundary models. Exemplar models of categorization (Medin and Schaffer 1978; Hintzman 1986; Nosofsky 1986) posit that people represent categories by storing individual exemplars of the category in memory. When encountering a novel item, their classification decision depends on the similarity of that item to every stored exemplar. Categorization relies upon memory for these exemplars, which is presumably the same memory that allows for the recognition of those exemplars (Nosofsky 1991). One challenge to exemplar theory comes from the performance of amnesic patients on a