The work in the Section on Learning and Decision Making focuses on understanding the computational mechanisms and neural circuitry that underlies reinforcement learning. Reinforcement learning is the behavioral process of associating objects or actions with rewards or punishments. More colloquially, the lab tries to understand how we learn on the basis of experience to have preferences for certain decisions. The lab uses a combination of high-channel count neurophysiology, lesions, behavioral pharmacology and computational modeling. We also collaborate with a number of clinical labs to examine changes in decision making processes in clinical populations thought to have pathology in the neural circuitry we study. In this work we maintain close similarity between the behaviors studied in the clinical groups, and those studied in animals.
Current theories of the neural circuitry that underlies reinforcement learning strongly implicate dopamine, and particularly the dopamine input to the striatum in these behaviors. However, recent work from our lab suggests that the amygdala may also play an important role in these processes, perhaps a more important role than the striatum. Therefore, current and near future work in the lab is focused on understanding the relative contribution of the amygdala, striatum, and frontal-striatal circuits, to these processes.