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Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates

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Title: Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates
Language: English
Authors: Solway, Alec, Botvinick, Matthew M.
Source: Psychological Review. Jan 2012 119(1):120-154.
Availability: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Peer Reviewed: Y
Physical Description: PDF
Page Count: 35
Publication Date: 2012
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Habit Formation, Brain, Decision Making, Rewards, Developmental Psychology, Outcomes of Education, Cognitive Science, Theories, Models, Prediction, Computation, Bayesian Statistics, Mathematical Concepts
DOI: 10.1037/a0026435
ISSN: 0033-295X
Abstract: 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. (Contains 17 footnotes, 1 table, and 12 figures.)
Abstractor: As Provided
Number of References: 293
Entry Date: 2012
Accession Number: EJ953539
Database: ERIC
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  Data: 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. (Contains 17 footnotes, 1 table, and 12 figures.)
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      – SubjectFull: Bayesian Statistics
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