DC Field | Value | Language |
---|---|---|
dc.contributor.author | O'Doherty, John | ko |
dc.contributor.author | Lee, Sang Wan | ko |
dc.contributor.author | Mcnamee, Daniel | ko |
dc.date.accessioned | 2016-05-12T03:02:41Z | - |
dc.date.available | 2016-05-12T03:02:41Z | - |
dc.date.created | 2016-02-12 | - |
dc.date.created | 2016-02-12 | - |
dc.date.issued | 2015-02 | - |
dc.identifier.citation | Current Opinion in Behavioral Sciences, v.1, pp.94 - 100 | - |
dc.identifier.issn | 2352-1546 | - |
dc.identifier.uri | http://hdl.handle.net/10203/207213 | - |
dc.description.abstract | Here we review recent developments in the application of reinforcement-learning theory as a means of understanding how the brain learns to select actions to maximize future reward, with a focus on human neuroimaging studies. We evaluate evidence for the distinction between model-based and model-free reinforcement-learning and their arbitration, and consider hierarchical reinforcement-learning schemes and structure learning. Finally we discuss the possibility of integrating across these different domains as a means of gaining a more complete understanding of how it is the brain learns from reinforcement. | - |
dc.language | English | - |
dc.publisher | Elsevier Limited | - |
dc.title | The structure of reinforcement-learning mechanisms in the human brain | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-84920069670 | - |
dc.type.rims | ART | - |
dc.citation.volume | 1 | - |
dc.citation.beginningpage | 94 | - |
dc.citation.endingpage | 100 | - |
dc.citation.publicationname | Current Opinion in Behavioral Sciences | - |
dc.identifier.doi | 10.1016/j.cobeha.2014.10.004 | - |
dc.contributor.localauthor | Lee, Sang Wan | - |
dc.contributor.nonIdAuthor | O'Doherty, John | - |
dc.contributor.nonIdAuthor | Mcnamee, Daniel | - |
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