Yael Niv, Ph.D, Professor of Psychology & Neuroscience
Princeton Psychology Department and Princeton Neuroscience Institute
Topic: Carving the world into useful task representations
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 states 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 represents these states, and is especially critical in tasks whose states must be inferred from partial cues together with internal information, for instance from working memory. If time permits, I will also discuss a potential role for hippocampal replay in honing these orbitofrontal state representations.