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PMID: 15235607 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops.

Nature neuroscience ·Vol. 7 ·No. 8 ·2004-08-00 ·Pages 887-93

Tanaka SC, Doya K, Okada G, Ueda K, Okamoto Y, Yamawaki S

Abstract

Evaluation of both immediate and future outcomes of one's actions is a critical requirement for intelligent behavior. Using functional magnetic resonance imaging (fMRI), we investigated brain mechanisms for reward prediction at different time scales in a Markov decision task. When human subjects learned actions on the basis of immediate rewards, significant activity was seen in the lateral orbitofrontal cortex and the striatum. When subjects learned to act in order to obtain large future rewards while incurring small immediate losses, the dorsolateral prefrontal cortex, inferior parietal cortex, dorsal raphe nucleus and cerebellum were also activated. Computational model-based regression analysis using the predicted future rewards and prediction errors estimated from subjects' performance data revealed graded maps of time scale within the insula and the striatum: ventroanterior regions were involved in predicting immediate rewards and dorsoposterior regions were involved in predicting future rewards. These results suggest differential involvement of the cortico-basal ganglia loops in reward prediction at different time scales.

MeSH Terms
Adult Brain/physiology Brain Mapping Discrimination Learning/physiology Female Humans Magnetic Resonance Imaging Male Markov Chains Middle Aged Reward
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Tanaka Saori C
Department of Bioinformatics and Genomics, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan.
Doya Kenji
Okada Go
Ueda Kazutaka
Okamoto Yasumasa
Yamawaki Shigeto
Article Info
Journal
Nature neuroscience
Abbr.
Nat Neurosci
ISSN
1097-6256
Published
2004-08-00
Epub
2004-00-04
Pages
887-93
Language
English
Region
United States
NLM ID
9809671
Subset
IM
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