Cognitive and Translational Neuroscience of Category Learning
A major focus of our research is to examine the computational and neurobiological underpinnings of category learning. We achieve this goal through a blending of empirical data collection, cognitive neuroscience, and mathematical modeling. In collaboration with Dr. Greg Ashby, a computational 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 and system level interactions predicted from the proposed neurobiological theory. We have also started an exciting new line of work that examines methods for enhancing learning and long-term retention and methods for enhancing true unlearning. This work has powerful implications for addiction and education. We recently began a project to explore skill learning in cases where the feedback is presented following a sequence of responses, as opposed to following each response.
We are also very interested in understanding the nature of category learning across the lifespan. In collaboration with Dr. Cynthia Huang-Pollack, we are examining category learning in children with and without ADHD. We are also very interested in category learning in healthy older adults. In particular we are interested in understanding how cognitive control and emotional processing in healthy aging affects category learning. We find very different relationships between learning and cognitive monitoring on older adults relative to younger adults, with this often leading to older adult learning advantages. We respect to emotional processing, we find that the well-established rule-based and set shifting deficits in normal aging can be attenuated, and in some case reversed. We are currently extending this work to a broad range of category structures, such as information-integration categories.
In collaboration with Dr. Chris Beevers, a clinical psychologist and expert in depression, we have begun to examine how depression affects category learning. In a related line of work, we are exploring the serotonergic and dopaminergic underpinnings of category learning. Finally, in collaboration with Dr. Bharath Chandrasekaran, we have begun to apply the dual systems framework made popular in vision to the auditory and speech domains. We find a remarkable similarity between visual and auditory category learning, but with some important caveats. We are examining changes in auditory and speech category learning across the lifespan and in special populations, such as musicians.
Cognitive and Translational Neuroscience of Decision Making
A major focus of our research is to examine the computational and neurobiological underpinnings of these types of decisions. In this work, we examine history-independent decision making in which the rewards available on the current choice are independent of the previous choice history, and compare that with history-dependent decision making in which the currently available rewards are dependent upon the previous reward history. In these tasks individuals select from one of many options with the aim of maximizing reward or minimizing losses. Under some condition the optimal long run strategy is to select the option that also maximizes short run gain (history-independent). Under other conditions, the optimal long run strategy is to forgo the short run maximizing option and instead to explore other options (history-dependent).
In collaboration with Dr. Darrell Worthy we are examining history-independent and history-dependent decision making in healthy younger adults and healthy older adults. Interestingly, we find that older adults are often better than younger adults at history-dependent decision making, but that this advantage is fragile. We are currently exploring methods for enhancing history-independent decision-making in older adults. With Dr. Russ Poldrack we are exploring the neural underpinnings of the age-related history-dependent decision making advantage. In all of this work, we rely heavily on computational modeling to provide insights onto cognitive processing.
In collaboration with Dr. Chris Beevers we are examining decision making in individuals high and low in depression. Interestingly, when the goal is to maximize rewards, we see a strong performance deficit for depressives, but when the goal is to minimize losses, we see a strong performance advantage for depressives. We are currently exploring the boundary conditions associated with this performance pattern and are exploring methods for attenuating the reward process deficit associated with depression. We are also examining performance in individuals with naturally occurring variation in serotonin and dopamine genetics. In collaboration with Dr. Vince Filoteo, a cognitive neuropsychologist, we examine decision-making in patients with striatal lesions (in particular patients with Parkinsonís disease). We have begun to examine how motivational influences, apathy and depression affect decision-making in PD.