Gershman, Samuel J., Moustafa, Ahmed A. and Ludvig, Elliot Andrew (2014) Time representation in reinforcement learning models of the basal ganglia. Frontiers in Computational Neuroscience, Volume 7 . Article number 194. doi:10.3389/fncom.2013.00194 ISSN 1662-5188.

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Abstract

Reinforcement learning (RL) models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between RL models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both RL and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

Item Type:	Journal Article
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QP Physiology
Divisions:	Faculty of Science, Engineering and Medicine > Science > Psychology
Library of Congress Subject Headings (LCSH):	Basal ganglia -- Physiology, Reinforcement learning -- Mathematical models, Dopamine, Time perception, Parkinson's disease -- Research
Journal or Publication Title:	Frontiers in Computational Neuroscience
Publisher:	Frontiers Research Foundation
ISSN:	1662-5188
Official Date:	9 January 2014
Dates:	Date Event 9 January 2014 Published
Volume:	Volume 7
Page Range:	Article number 194
DOI:	10.3389/fncom.2013.00194
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	26 December 2015
Date of first compliant Open Access:	26 December 2015
Funder:	United States. Department of the Interior (DOI), Massachusetts Institute of Technology (MIT), University of Western Sydney (UWS), National Institutes of Health (U.S.) (NIH), Princeton University. Princeton Pyne Fund
Grant number:	D10PC2002 (DOI), P00021210 (UWS), P30AG024361 (NIH)

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