期刊
PSYCHOLOGICAL REVIEW
卷 119, 期 1, 页码 120-154出版社
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0026435
关键词
decision making; planning; reward; probabilistic inference; neuroeconomics
资金
- McDonnell Foundation
- Institutional National Research Service [T32 MH065214]
- NATIONAL INSTITUTE OF MENTAL HEALTH [T32MH065214] Funding Source: NIH RePORTER
Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology, and machine learning to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection.
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