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

Autor/inn/en	Solway, Alec; Botvinick, Matthew M.
Titel	Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates
Quelle	In: Psychological Review, 119 (2012) 1, S.120-154 (35 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0026435
Schlagwörter	Habit Formation; Brain; Decision Making; Rewards; Developmental Psychology; Outcomes of Education; Cognitive Science; Theories; Models; Prediction; Computation; Bayesian Statistics; Mathematical Concepts + Suchen Sie Ihr Suchwort? Gehirn; Decision-making; Entscheidungsfindung; Reward; Belohnung; Entwicklungspsychologie; Lernleistung; Schulerfolg; Kognitionswissenschaft; Theory; Theorie; Analogiemodell; Vorhersage
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.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10