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Time representation in reinforcement learning models of the basal ganglia
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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
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WRAP_Ludvig_fncom-07-00194.pdf - Published Version Available under License Creative Commons Attribution. Download (1170Kb) | Preview |
Official URL: http://dx.doi.org/10.3389/fncom.2013.00194
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 | ||||
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| Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > QP Physiology |
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| Divisions: | Faculty of 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 | ||||
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| 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 | ||||
| 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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