Thinking the right thoughts

This seminar was presented by Professor Nathaniel Daw, Huo Professor in Theoretical and Computational Neuroscience in the Princeton Neuroscience Institute and the Department of Psychology at Princeton University.
Please note the recording starts two minutes into the talk.
ABSTRACT
In realistic choice tasks, especially sequential ones like mazes, actions are separated from their consequences by many steps of space and time. A central computational problem in decision making - which arises in various guises such as credit assignment and planning - is spanning these gaps to work out the long-term consequences of candidate actions. I review recent experimental and theoretical work aimed at understanding the mechanisms by which the brain solves this problem. First, I review a new study that monitors neural signatures of reward expectancy in rodents to monitor how the brain propagates information about individual experiences with outcomes to distal choicepoints. Second, I report ongoing theoretical work that aims to clarify how the brain can judiciously manage and select among such computations so as to achieve effective decisions while minimizing computational costs. This offers a formal, resource-rational perspective on a range of issues such as habits and slips of action in the healthy brain, but also may explain dysfunctions such as compulsion, rumination, and avoidance.
ABOUT THE SPEAKER
Nathaniel Daw is Huo Professor in Theoretical and Computational Neuroscience in the Princeton Neuroscience Institute and the Department of Psychology at Princeton University and a Visiting Staff Research Scientist at DeepMind. He received his Ph.D. in computer science from Carnegie Mellon University and at the Center for the Neural Basis of Cognition, before conducting postdoctoral research at the Gatsby Computational Neuroscience Unit at UCL. He served on the faculty at New York University before coming to Princeton. His research concerns computational approaches to reinforcement learning and decision making, and particularly the application of computational models in the laboratory, to the design of experiments and the analysis of behavioral and neural data.