Linked Life Data

17215398

Source:http://linkedlifedata.com/resource/pubmed/id/17215398

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2007-1-11
pubmed:abstractText
Optimal behavior in a competitive world requires the flexibility to adapt decision strategies based on recent outcomes. In the present study, we tested the hypothesis that this flexibility emerges through a reinforcement learning process, in which reward prediction errors are used dynamically to adjust representations of decision options. We recorded event-related brain potentials (ERPs) while subjects played a strategic economic game against a computer opponent to evaluate how neural responses to outcomes related to subsequent decision-making. Analyses of ERP data focused on the feedback-related negativity (FRN), an outcome-locked potential thought to reflect a neural prediction error signal. Consistent with predictions of a computational reinforcement learning model, we found that the magnitude of ERPs after losing to the computer opponent predicted whether subjects would change decision behavior on the subsequent trial. Furthermore, FRNs to decision outcomes were disproportionately larger over the motor cortex contralateral to the response hand that was used to make the decision. These findings provide novel evidence that humans engage a reinforcement learning process to adjust representations of competing decision options.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1529-2401
pubmed:author
pubmed:issnType
Electronic
pubmed:day
10
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
371-8
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Reinforcement learning signals predict future decisions.
pubmed:affiliation
Department of Epileptology and Center for Mind and Brain, University of Bonn, 53105 Bonn, Germany. mcohen@ucdavis.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural