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Combined model-free and model-sensitive reinforcement learning in non-human primates

Abstract:

Contemporary reinforcement learning (RL) theory suggests that potential choices can be evaluated by strategies that may or may not be sensitive to the computational structure of tasks. A paradigmatic model-free (MF) strategy simply repeats actions that have been rewarded in the past; by contrast, model-sensitive (MS) strategies exploit richer information associated with knowledge of task dynamics. MF and MS strategies should typically be combined, because they have complementary statistical a...

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Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0003-4660-6051
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Role:
Author
ORCID:
0000-0003-2083-2261
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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
ORCID:
0000-0003-0048-1177
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Role:
Author
ORCID:
0000-0003-3476-1839
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Role:
Author
ORCID:
0000-0002-5696-7507
Publisher:
Public Library of Science
Journal:
PLoS Computational Biology More from this journal
Volume:
16
Issue:
6
Article number:
e1007944
Publication date:
2020-06-22
Acceptance date:
2020-05-12
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
Pmid:
32569311
Language:
English
Keywords:
Pubs id:
1114873
Local pid:
pubs:1114873
Deposit date:
2020-10-22

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