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dc.contributor.authorAlper, Sinan
dc.contributor.authorBayrak, Fatih
dc.contributor.authorYilmaz, Onurcan
dc.date.accessioned2020-11-30T09:48:11Z
dc.date.available2020-11-30T09:48:11Z
dc.date.issued2021
dc.identifier.issn0191-8869
dc.identifier.urihttps://doi.org/10.1016/j.paid.2020.110350en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3482
dc.description.abstractSome of the recent studies suggested that people can make accurate inferences about the level of the Big Five and the Dark Triad personality traits in strangers by only looking at their faces. However, later findings provided only partial support and the evidence is mixed regarding which traits can be accurately inferred from faces. In the current research, to provide further evidence on whether the Big Five and the Dark Triad traits are visible in the face, we report three studies, two of which were preregistered, conducted on both WEIRD (the US American) and non-WEIRD (Turkish) samples (N = 880). The participants in both the US American and Turkish samples were successful in predicting all Dark Triad personality traits by looking at a stranger's face. However, there were mixed results regarding the Big Five traits. An aggregate analysis of the combined dataset demonstrated that extraversion (only female), agreeableness, and conscientiousness were accurately inferred by the participants in addition to the Dark Triad traits. Overall, the results suggest that inferring personality from faces without any concrete source of information might be an evolutionarily adaptive trait.en_US
dc.language.isoEnglishen_US
dc.publisherPergamon-Elsevıer Scıence Ltden_US
dc.subjectBig Fiveen_US
dc.subjectDark Triaden_US
dc.subjectFace perceptionen_US
dc.subjectPersonalityen_US
dc.subjectZero acquaintanceen_US
dc.titleAll the Dark Triad and some of the Big Five traits are visible in the faceen_US
dc.typeArticleen_US
dc.relation.journalPersonalıty and Indıvıdual Dıfferencesen_US
dc.identifier.issue168en_US
dc.identifier.wosWOS:000580635900067en_US
dc.identifier.doi10.1016/j.paid.2020.110350en_US
dc.contributor.khasauthorYilmaz, Onurcanen_US


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