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dc.contributor.authorLengyel, Gábor
dc.contributor.authorNagy, Márton
dc.contributor.authorFiser, József
dc.date.accessioned2023-06-16T14:43:22Z
dc.date.available2023-06-16T14:43:22Z
dc.date.issued2021
dc.identifier.issn2041-1723
dc.identifier.doi10.1038/s41467-020-20589-z
dc.identifier.urihttp://hdl.handle.net/20.500.14018/13923
dc.description.abstractAlthough objects are the fundamental units of our representation interpreting the environment around us, it is still not clear how we handle and organize the incoming sensory information to form object representations. By utilizing previously well-documented advantages of within-object over across-object information processing, here we test whether learning involuntarily consistent visual statistical properties of stimuli that are free of any traditional segmentation cues might be sufficient to create object-like behavioral effects. Using a visual statistical learning paradigm and measuring efficiency of 3-AFC search and object-based attention, we find that statistically defined and implicitly learned visual chunks bias observers’ behavior in subsequent search tasks the same way as objects defined by visual boundaries do. These results suggest that learning consistent statistical contingencies based on the sensory input contributes to the emergence of object representations.
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleStatistically defined visual chunks engage object-based attention
dc.typeJournal article
dc.source.journaltitleNature Communications
dc.source.volume12
dc.source.issue1
dc.source.spage1
dc.source.epage12
dc.description.versionPublished version
refterms.dateFOA2023-06-18T11:09:31Z
dc.contributor.unitDepartment of Cognitive Science
dc.source.journalabbrevNat Commun
dc.identifier.urlhttps://www.nature.com/articles/s41467-020-20589-z


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