Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning

Abstract Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning or by using explicit reasoning. We tested if the striatum, in which activation reliably scales with...

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Bibliographic Details
Published in:Cerebral cortex (New York, N.Y. 1991) N.Y. 1991), 2018-11, Vol.28 (11), p.3965-3975
Main Authors: Ballard, Ian, Miller, Eric M, Piantadosi, Steven T, Goodman, Noah D, McClure, Samuel M
Format: Article
Language:English
Subjects:
Adult
Association Learning
Bayes Theorem
Brain Mapping
Corpus Striatum - physiology
Female
Humans
Magnetic Resonance Imaging
Male
Models, Neurological
Models, Psychological
Original
Reward
Young Adult
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Summary:Abstract Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning or by using explicit reasoning. We tested if the striatum, in which activation reliably scales with reward prediction error, would track prediction errors in a task that required explicit rule generation. Using functional magnetic resonance imaging during a categorization task, we show that striatal responses to feedback scale with a “surprise” signal derived from a Bayesian rule-learning model and are inconsistent with RL prediction error. We also find that striatum and caudal inferior frontal sulcus (cIFS) are involved in updating the likelihood of discriminative rules. We conclude that the striatum, in cooperation with the cIFS, is involved in updating the values assigned to categorization rules when people learn using explicit reasoning.
ISSN:1047-3211
1460-2199
DOI:10.1093/cercor/bhx259