The structure of reinforcement-learning mechanisms in the human brain

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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.
Publisher
Elsevier Limited
Issue Date
2015-02
Language
English
Citation

Current Opinion in Behavioral Sciences, v.1, pp.94 - 100

ISSN
2352-1546
DOI
10.1016/j.cobeha.2014.10.004
URI
http://hdl.handle.net/10203/207213
Appears in Collection
BiS-Journal Papers(저널논문)
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