• Wednesday, September 12th 2018 at 15:00 - 16:00 UTC (Other timezones)
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The notion of competition between “model-free” and “model-based” reinforcement learning systems has become an important theme in the study of decision making, with numerous applications to psychiatry. I discuss new data suggesting that competition is arbitrated by a cost-benefit metacontrol algorithm, weighing the benefits of model-based accuracy against the cognitive costs of its execution. In a large sample from the general population, many measures of psychopathology show deficits in model-based control, but importantly these deficits can be ameliorated by manipulating the incentives.

 

 

 

 

 

 

 

Sam Gershman
Assistant Professor
Department of Psychology and Center for Brain Science
Harvard University

Sam Gershman – Metacontrol of reinforcement learning across the psychopathology spectrum