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Abstract:
Substantial efforts across the fields of computer science, artificial intelligence, statistics, operations research, economics, and control theory have provided us with a psychologically- and neurobiologically-grounded account of how humans and other animals learn to predict rewards and punishments, and choose actions to maximize the former and minimize the latter. It becomes an obvious idea to try and relate disruptions of these models to the discontents of decision-making, as seen in neurological and psychiatric disease. I will describe Bayesian accounts of decision making, along with various reinforcement learning realizations, together with our early attempts to use this to structure an understanding of dysfunction.
