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The Efficiency of Demography in Face Perception.
Colombatto, Clara; Uddenberg, Stefan; Scholl, Brian J.
Afiliação
  • Colombatto C; Department of Psychology, Yale University, Box 208205, New Haven, CT, 06520-8205, USA.
  • Uddenberg S; Booth School of Business, University of Chicago, 5807 S. Woodlawn Ave.,, Chicago, IL, 60637, USA.
  • Scholl BJ; Department of Psychology, Yale University, Box 208205, New Haven, CT, 06520-8205, USA. brian.scholl@yale.edu.
Atten Percept Psychophys ; 83(8): 3104-3117, 2021 Nov.
Article em En | MEDLINE | ID: mdl-34427904
ABSTRACT
When we look at a face, we cannot help but "read" it Beyond simply processing its identity, we also form robust impressions of both transient psychological states (e.g., surprise) and stable character traits (e.g., trustworthiness). But perhaps the most fundamental traits we extract from faces are their social demographics, for example, race, age, and gender. How much exposure is required to extract such properties? Curiously, despite extensive work on the temporal efficiency of extracting both higher-level social properties (such as competence and dominance) and more basic characteristics (such as identity and familiarity), this question remains largely unexplored for demography. We correlated observers' percepts of the race/age/gender of unfamiliar faces viewed at several brief durations (and then masked) with their judgments after unlimited exposure. Performance reached asymptote by 100 ms, was above chance by only 33.33 ms, and had a similar temporal profile to detecting faces in the first place. This was true even when the property to be reported wasn't revealed until after the face had disappeared, and when the faces were matched for several lower-level visual properties. Collectively, these results demonstrate that the extraction of demographic features from faces is highly efficient, and can truly be done at a glance.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reconhecimento Facial Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reconhecimento Facial Idioma: En Ano de publicação: 2021 Tipo de documento: Article