Understanding neural coding through the model-based analysis of decision making.
The study of decision making poses new methodological challenges for systems neuroscience. Whereas our traditional approach linked neural activity to external variables that the experimenter directly observed and manipulated, many of the key elements that contribute to decisions are internal to the decider. Variables such as subjective value or subjective probability may be influenced by experimental conditions and manipulations but can neither be directly measured nor precisely controlled. Pioneering work on the neural basis of decision circumvented this difficulty by studying behavior in static conditions, in which knowledge of the average state of these quantities was sufficient. More recently, a new wave of studies has confronted the conundrum of internal decision variables more directly by leveraging quantitative behavioral models. When these behavioral models are successful in predicting a subject's choice, the model's internal variables may serve as proxies for the unobservable decision variables that actually drive behavior. This new methodology has allowed researchers to localize neural subsystems that encode hidden decision variables related to free choice and to study these variables under dynamic conditions.
Stanford University, Stanford, California 94305, USA. gcorrado@stanford.edu
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D000818:Animals; D032882:Comprehension / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D009415:Nerve Net / Q000502:physiology*