CSL Seminar - Sam Gershman - Reverse engineering human exploration
Host
Pulkit Agrawal
Abstract: This talk explores how humans solve the exploration-exploitation dilemma in reinforcement learning. Mirroring the multiplicity of algorithmic solutions studied in machine learning, the human brain also appears to employ several different algorithms. In particular, evidence indicates that humans use a combination of uncertainty-guided directed and random exploration, as well as more sophisticated algorithms that rely on structured world knowledge. These findings point towards a convergence of natural and artificial intelligence.
Bio: Sam Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013. From 2013-2015, he was a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT. He is currently a Professor in the Department of Psychology and Center for Brain Science at Harvard. His research focuses on computational cognitive neuroscience approaches to learning, memory and decision making.
Bio: Sam Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013. From 2013-2015, he was a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT. He is currently a Professor in the Department of Psychology and Center for Brain Science at Harvard. His research focuses on computational cognitive neuroscience approaches to learning, memory and decision making.