Abstract

The brain dynamically arbitrates between two model-based and model-free reinforcement learning (RL). Here, the authors show that participants tended to increase model-based control in response to increasing task complexity, but resorted to model-free when both uncertainty and task complexity were high.

Details

Title
Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning
Author
Kim, Dongjae 1   VIAFID ORCID Logo  ; Park, Geon Yeong 2 ; John P O′Doherty 3 ; Sang Wan Lee 4 

1  Department of Bio and Brain Engineering, Korea Advanced Institute of Science Technology (KAIST), Daejeon, Republic of Korea; Program of Brain and Cognitive Engineering, KAIST, Daejeon, Republic of Korea 
2  Department of Bio and Brain Engineering, Korea Advanced Institute of Science Technology (KAIST), Daejeon, Republic of Korea 
3  Computation & Neural Systems, California Institute of Technology, Pasadena, CA, USA; Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA 
4  Department of Bio and Brain Engineering, Korea Advanced Institute of Science Technology (KAIST), Daejeon, Republic of Korea; Program of Brain and Cognitive Engineering, KAIST, Daejeon, Republic of Korea; KI for Health Science Technology, KAIST, Daejeon, Republic of Korea; KI for Artificial Intelligence, KAIST, Daejeon, Republic of Korea 
Pages
1-14
Publication year
2019
Publication date
Dec 2019
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41467-019-13632-1
ProQuest document ID
2327337036
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.