Speaker Yael Niv, PhD Princeton University, Professor of Psychology and Neuroscience
Dr. Niv is associate professor of psychology and neuroscience at Princeton University. Her work investigates the neural and computational processes underlying reinforcement learning—the ongoing day-to-day processes by which we learn from trial and error to maximize reward and minimize punishment. She is the recipient of the 2015 National Academy of Sciences Troland Research Award, and the 2012 Presidential Early Career Award for Scientists and Engineers, is an Ellison Foundation Scholar and was an Alfred P. Sloan Research Fellow.
Abstract No two events are alike. But still, we learn, which means that we implicitly decide what events are similar enough that experience with one can inform us about what to do in another. We have suggested that this relies on an implicit parsing of incoming information into “clusters” according to inferred hidden (latent) causes. Moreover, we have suggested that unexpected information (that is, a prediction error) is key to this separation into clusters. In this talk, I will demonstrate these ideas through behavioral experiments showing evidence for clustering in animals and humans, and illustrating the effects of prediction errors on the organization of memory. I will end by tying the different findings together into a hypothesis about how information about events is organized in our brain.
About the Wu Tsai Neuro MBCT Seminar Series The Stanford Center for Mind, Brain, Computation and Technology Seminars (MBCT) explores ways in which computational and technical approaches are being used to advance the frontiers of neuroscience. It features speakers from other institutions, Stanford faculty and senior training program trainees.