Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

dc.contributor.author	Kümmerer, Matthias
dc.contributor.author	Wallis, Thomas S. A.
dc.contributor.author	Bethge, Matthias
dc.date.accessioned	2021-09-24T08:58:45Z
dc.date.available	2021-09-24T08:58:45Z
dc.date.issued	2021
dc.identifier.issn	1873-7838
dc.identifier.uri	http://hdl.handle.net/10900/119162
dc.language.iso	en	en
dc.publisher	Elsevier	de_DE
dc.relation.uri	http://dx.doi.org/10.1016/j.cognition.2020.104465
dc.subject.ddc	150	de_DE
dc.title	Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations	de_DE
dc.type	Article	de_DE
utue.quellen.id	20210512015105_01513
utue.personen.roh	Pedziwiatr, Marek A.
utue.personen.roh	Kuemmerer, Matthias
utue.personen.roh	Wallis, Thomas S. A.
utue.personen.roh	Bethge, Matthias
utue.personen.roh	Teufel, Christoph
dcterms.isPartOf.ZSTitelID	Cognition	de_DE
dcterms.isPartOf.ZS-Issue	Article 104465	de_DE
dcterms.isPartOf.ZS-Volume	206	de_DE
utue.fakultaet	Universität Tübingen (ohne Fakultätsangabe)