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Neural basis of reinforcement learning and decision making

Authors
Lee, D; Seo, H; Jung, MW
Citation
Annual review of neuroscience, 35:287-308, 2012
Journal Title
Annual review of neuroscience
ISSN
0147-006X1545-4126
Abstract
Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal's knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.
MeSH terms
AnimalsBrain Mapping/*psychologyDecision Making/*physiologyEconomics, BehavioralHumansLearning/*physiologyModels, NeurologicalModels, PsychologicalNeural Networks (Computer)*Reinforcement (Psychology)
DOI
10.1146/annurev-neuro-062111-150512
PMID
22462543
Appears in Collections:
Journal Papers > Research Organization > Institute for Medical Sciences
AJOU Authors
정, 민환
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