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dc.contributor.authorWang, Ren
dc.contributor.authorShen, Yen
dc.contributor.authorTino, Pen
dc.contributor.authorWelchman, Andrewen
dc.contributor.authorKourtzi, Zoeen
dc.date.accessioned2017-10-26T14:51:33Z
dc.date.available2017-10-26T14:51:33Z
dc.date.issued2017-10en
dc.identifier.issn1534-7362
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/267940
dc.description.abstractHuman behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity: from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences: from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment’s statistics; that is, they extract the behaviorally-relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selecting the most probable outcome in a given context (maximizing) rather than matching the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally-relevant statistics that facilitate our ability to predict future events in variable environments.
dc.description.sponsorshipThis work was supported by grants to PT from the Engineering and Physical Sciences Research Council (EP/L000296/1); to ZK from the Biotechnology and Biological Sciences Research Council (H012508), the Leverhulme Trust (RF-2011-378), and the European Community's Seventh Framework Programme (FP7/2007-2013) under agreement PITN-GA-2011-290011; and to AW from the Wellcome Trust (095183/Z/10/Z).
dc.publisherAssociation for Research in Vision and Ophthalmology
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleLearning predictive statistics from temporal sequences: Dynamics and strategiesen
dc.typeArticle
prism.issueIdentifier12en
prism.publicationDate2017en
prism.publicationNameJournal of Visionen
prism.volume17en
dc.identifier.doi10.17863/CAM.13873
dcterms.dateAccepted2017-08-26en
rioxxterms.versionofrecord10.1167/17.12.1en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-10en
dc.contributor.orcidWelchman, Andrew [0000-0002-7559-3299]
dc.contributor.orcidKourtzi, Zoe [0000-0001-9441-7832]
dc.identifier.eissn1534-7362
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idWellcome Trust (095183/B/10/Z)
pubs.funder-project-idBBSRC (BB/P021255/1)
pubs.funder-project-idEuropean Commission (290011)
pubs.funder-project-idESRC (ES/M500409/1)
pubs.funder-project-idLeverhulme Trust (RF-2011-378)
cam.issuedOnline2017-10-02en


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