Time representation in reinforcement learning models of the basal ganglia
Name
Gershman-2014-Time representation.pdf
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
11d2e8c4d63823ebf8b892a97e45d7cb
Author(s)
Gershman, Samuel J.
Moustafa, Ahmed A.
Ludvig, Elliot A.
Date Issued
January 2014
Journal
Frontiers in Computational Neuroscience
Publisher
Frontiers Research Foundation
Citation
Gershman, Samuel J., Ahmed A. Moustafa, and Elliot A. Ludvig. “Time Representation in Reinforcement Learning Models of the Basal Ganglia.” Frontiers in Computational Neuroscience 7 (2014).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fncom.2013.00194
