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: | |
| 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 |
| 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.) |
|---|---|
| ISSN: | 0033-295X |
| DOI: | 10.1037/a0026435 |
