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dc.contributor.authorBays, Paulen
dc.contributor.authorDowding, BAen
dc.date.accessioned2017-05-11T14:27:22Z
dc.date.available2017-05-11T14:27:22Z
dc.date.issued2017-03-01en
dc.identifier.issn1553-734X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/264188
dc.description.abstractThe ability to make optimal decisions depends on evaluating the expected rewards associated with different potential actions. This process is critically dependent on the fidelity with which reward value information can be maintained in the nervous system. Here we directly probe the fidelity of value representation following a standard reinforcement learning task. The results demonstrate a previously-unrecognized bias in the representation of value: extreme reward values, both low and high, are stored significantly more accurately and precisely than intermediate rewards. The symmetry between low and high rewards pertained despite substantially higher frequency of exposure to high rewards, resulting from preferential exploitation of more rewarding options. The observed variation in fidelity of value representation retrospectively predicted performance on the reinforcement learning task, demonstrating that the bias in representation has an impact on decision-making. A second experiment in which one or other extreme-valued option was omitted from the learning sequence showed that representational fidelity is primarily determined by the relative position of an encoded value on the scale of rewards experienced during learning. Both variability and guessing decreased with the reduction in the number of options, consistent with allocation of a limited representational resource. These findings have implications for existing models of reward-based learning, which typically assume defectless representation of reward value.
dc.description.sponsorshipThis research was supported by the Wellcome Trust (grant number 106926 to PMB).
dc.languageengen
dc.language.isoenen
dc.publisherPLOS
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectlearningen
dc.subjectdecision makingen
dc.subjectneuronsen
dc.subjectmemoryen
dc.subjectnormal distributionen
dc.subjectfractalsen
dc.subjectlearning curvesen
dc.subjectworking memoryen
dc.titleFidelity of the representation of value in decision-makingen
dc.typeArticle
prism.issueIdentifier3en
prism.numbere1005405en
prism.publicationDate2017en
prism.publicationNamePLOS Computational Biologyen
prism.volume13en
dc.identifier.doi10.17863/CAM.9547
dcterms.dateAccepted2017-02-13en
rioxxterms.versionofrecord10.1371/journal.pcbi.1005405en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-03-01en
dc.contributor.orcidBays, Paul [0000-0003-4684-4893]
dc.identifier.eissn1553-7358
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idWELLCOME TRUST (106926/Z/15/Z)


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International