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dc.contributor.authorHarrison, William Jen
dc.contributor.authorMcMaster, Jessicaen
dc.contributor.authorBays, Paulen
dc.date.accessioned2021-05-05T23:30:11Z
dc.date.available2021-05-05T23:30:11Z
dc.date.issued2021-05-29en
dc.identifier.issn0010-0277
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/322001
dc.description.abstractAccounts of working memory based on independent item representations may overlook a possible contribution of ensemble statistics, higher-order regularities of a scene such as the mean or variance of a visual attribute. Here we used change detection tasks to investigate the hypothesis that observers store ensemble statistics in working memory and use them to detect changes in the visual environment. We controlled changes to the ensemble mean or variance between memory and test displays across six experiments. We made specific predictions of observers’ sensitivity using an optimal summation model that integrates evidence across separate items but does not detect changes in ensemble statistics. We found strong evidence that observers outperformed this model, but only when task difficulty was high, and only for changes in stimulus variance. Under these conditions, we estimated that the variance of items contributed to change detection sensitivity more strongly than any individual item in this case. In contrast, however, we found strong evidence against the hypothesis that the average feature value is stored in working memory: when the mean of memoranda changed, sensitivity did not differ from the optimal summation model, which was blind to the ensemble mean, in five out of six experiments. Our results reveal that change detection is primarily limited by uncertainty in the memory of individual features, but that memory for the variance of items can facilitate detection under a limited set of conditions that involve relatively high working memory demands.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleLimited memory for ensemble statistics in visual change detection.en
dc.typeArticle
prism.publicationDate2021en
prism.publicationNameCognitionen
prism.startingPage104763
prism.volume214en
dc.identifier.doi10.17863/CAM.69459
dcterms.dateAccepted2021-05-03en
rioxxterms.versionofrecord10.1016/j.cognition.2021.104763en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-05-29en
dc.contributor.orcidBays, Paul [0000-0003-4684-4893]
dc.identifier.eissn1873-7838
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idWELLCOME TRUST (106926/Z/15/Z)
cam.orpheus.successMon Jun 07 07:30:50 BST 2021 - Embargo updated*
cam.orpheus.counter4*
rioxxterms.freetoread.startdate2022-05-29


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