Dr. Yael Niv Princeton University
What is the role of the orbitofrontal cortex in reinforcement learning?
In recent years ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories about the effects of dopamine on learning in the basal ganglia. However, the first ingredient in any reinforcement learning algorithm is a representation of the task as a sequence of states. Where do these state representations come from? In this talk I will first argue, and demonstrate using behavioral experiments, that animals and humans learn the latent structure of a task, thus forming a state space through experience. I will then suggest that the orbitofrontal cortex is critical to representing these state spaces, especially in tasks whose latent structure is important for correct performance, and demonstrate how this hypothesis can explain extant data from studies in which orbitofrontal function was compromised. Finally, I will present data from a new study in which we use representation similarity analysis and graph theory to map the representation of sixteen task states in the human orbitofrontal cortex.
Thursday, February 26, 2015 6:00-7:30 PM 207 Warren Hall (1125 Amsterdam Ave)