Neurobiology of Category Learning and Attention  A major focus of our research is to examine the neurobiological underpinnings of category learning and attentional processes.  We achieve this goal through a blending of empirical data collection, cognitive neuroscience, and mathematical modeling. In collaboration with Dr. Vince Filoteo, a cognitive neuropsychologist, we examine category learning and attentional processes in patients with striatal lesions (in particular patients with Parkinson’s disease). In category learning, our focus is on dissociating performance in rule-based category learning tasks that can be solved by applying some explicit reasoning process (e.g., the small objects are in category A and the large objects are in category B) and is proposed to rely on frontal brain areas, from performance in information-integration category learning tasks that are learned via a gradual incremental (procedural) learning system and is proposed to rely on subcortical structures in the striatum and a dopamine mediated reward signal. In attention, our focus is on dissociating performance across tasks that require selective attention versus those that require attention to multiple stimulus aspects, and in dissociating executive, or decisional attention processes from perceptual attention processes. In collaboration with Dr. Greg Ashby, a cognitive neuroscientist, we test a priori predictions from a neurobiological model of category learning that postulates an important role of working memory in rule-based category learning, and an important role of dopamine mediated procedural learning in information-integration category learning. These studies are generally conducted on college-students and aim to test rule-based and information-integration category learning dissociations predicted from the proposed neurobiological theory. A number of dissociations have already been tested including a predicted effect of delayed feedback and the type of response (A/B vs. Yes/No) on information-integration, but not rule-based category learning (more info). We are extending these studies in a number of ways by examining a number of rule-based and information-integration category structures, examining a wide range of feedback delays, and the effects of the number of categories on performance. Currently, we are also testing the effects of several factors predicted to affect rule-based, but not information-integration category learning. [main page]