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dc.contributor.authorRam, Vishaal
dc.contributor.authorSchaposnik, Laura P.
dc.contributor.authorKonstantinou, Nikos
dc.contributor.authorVolkan, Eliz
dc.contributor.authorPapadatou-Pastou, Marietta
dc.contributor.authorManav, Banu
dc.contributor.authorJonauskaite, Domicele
dc.contributor.authorMohr, Christine
dc.date.accessioned2021-01-28T13:03:56Z
dc.date.available2021-01-28T13:03:56Z
dc.date.issued2020
dc.identifier.issn2643-1564
dc.identifier.urihttps://doi.org/10.1103/PhysRevResearch.2.033350en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3789
dc.description.abstractBy means of an experimental dataset, we use deep learning to implement an RGB (red, green, and blue) extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males (type-m individuals) typically associate a given emotion with darker colors, while females (typef individuals) associate it with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors.en_US
dc.description.sponsorshipNational Science Foundation (NSF) Swiss National Science Foundation (SNSF)en_US
dc.language.isoEnglishen_US
dc.publisherAmer Physical Socen_US
dc.subjectAssociationsen_US
dc.subjectPreferencesen_US
dc.subjectAgeen_US
dc.titleExtrapolating continuous color emotions through deep learningen_US
dc.typeArticleen_US
dc.relation.journalPhysical Review Researchen_US
dc.identifier.issue3en_US
dc.identifier.volume2en_US
dc.identifier.wosWOS:000604171000005en_US
dc.identifier.doi10.1103/PhysRevResearch.2.033350en_US
dc.contributor.khasauthorManav, Banuen_US


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