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dc.contributor.authorGreve, Andreaen
dc.contributor.authorCooper, Elisaen
dc.contributor.authorKaula, Aen
dc.contributor.authorAnderson, Mikeen
dc.contributor.authorHenson, Riken
dc.date.accessioned2017-07-20T10:26:58Z
dc.date.available2017-07-20T10:26:58Z
dc.date.issued2017-06en
dc.identifier.issn0749-596X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/265675
dc.description.abstractThe role of prediction error (PE) in driving learning is well-established in fields such as classical and instrumental conditioning, reward learning and procedural memory; however, its role in human one-shot declarative encoding is less clear. According to one recent hypothesis, PE reflects the divergence between two probability distributions: one reflecting the prior probability (from previous experiences) and the other reflecting the sensory evidence (from the current experience). Assuming unimodal probability distributions, PE can be manipulated in three ways: (1) the distance between the mode of the prior and evidence, (2) the precision of the prior, and (3) the precision of the evidence. We tested these three manipulations across five experiments, in terms of peoples' ability to encode a single presentation of a scene-item pairing as a function of previous exposures to that scene and/or item. Memory was probed by presenting the scene together with three choices for the previously paired item, in which the two foil items were from other pairings within the same condition as the target item. In Experiment 1, we manipulated the evidence to be either consistent or inconsistent with prior expectations, predicting PE to be larger, and hence memory better, when the new pairing was inconsistent. In Experiments 2a-c, we manipulated the precision of the priors, predicting better memory for a new pairing when the (inconsistent) priors were more precise. In Experiment 3, we manipulated both visual noise and prior exposure for unfamiliar faces, before pairing them with scenes, predicting better memory when the sensory evidence was more precise. In all experiments, the PE hypotheses were supported. We discuss alternative explanations of individual experiments, and conclude the Predictive Interactive Multiple Memory Signals (PIMMS) framework provides the most parsimonious account of the full pattern of results.
dc.languageengen
dc.language.isoenen
dc.publisherElsevier
dc.subjectAssociative memoryen
dc.subjectEncodingen
dc.subjectOne-shot learningen
dc.subjectPrediction erroren
dc.titleDoes prediction error drive one-shot declarative learning?en
dc.typeArticle
prism.endingPage165
prism.publicationDate2017en
prism.publicationNameJournal of Memory and Languageen
prism.startingPage149
prism.volume94en
dc.identifier.doi10.17863/CAM.11895
dcterms.dateAccepted2016-11-01en
rioxxterms.versionofrecord10.1016/j.jml.2016.11.001en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-06en
dc.contributor.orcidCooper, Elisa [0000-0003-3259-4408]
dc.contributor.orcidAnderson, Mike [0000-0001-9505-9299]
dc.contributor.orcidHenson, Rik [0000-0002-0712-2639]
dc.identifier.eissn1096-0821
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (unknown)
pubs.funder-project-idMedical Research Council (MC_U105579226)
cam.issuedOnline2016-11-19en


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