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Familiarity influences judgments of sex: The case of voice recognition

Identifieur interne : 001D46 ( Main/Exploration ); précédent : 001D45; suivant : 001D47

Familiarity influences judgments of sex: The case of voice recognition

Auteurs : A. Mike Burton [Royaume-Uni] ; Lesley Bonner [Royaume-Uni]

Source :

RBID : Pascal:04-0580317

Descripteurs français

English descriptors

Abstract

Two experiments are reported in which subjects made judgments about the sex or the familiarity of a voice. In experiment 1, subjects were fans of the BBC-radio soap opera, The Archers, and familiar voice clips were taken from this programme. Subjects showed a large reduction in response times when making sex judgments to familiar voices, despite the fact that sex judgments are generally much faster than familiarity judgments. In experiment 2, the same familiar clips were played to subjects unfamiliar with the soap opera, and no difference was observed in times to make sex judgments to Archers or non-Archers voices. We conclude that, unlike the case of face recognition, sex and identity processing of voices are not independent. The findings constrain models of person recognition across multiple modalities.


Affiliations:


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Le document en format XML

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<div type="abstract" xml:lang="en">Two experiments are reported in which subjects made judgments about the sex or the familiarity of a voice. In experiment 1, subjects were fans of the BBC-radio soap opera, The Archers, and familiar voice clips were taken from this programme. Subjects showed a large reduction in response times when making sex judgments to familiar voices, despite the fact that sex judgments are generally much faster than familiarity judgments. In experiment 2, the same familiar clips were played to subjects unfamiliar with the soap opera, and no difference was observed in times to make sex judgments to Archers or non-Archers voices. We conclude that, unlike the case of face recognition, sex and identity processing of voices are not independent. The findings constrain models of person recognition across multiple modalities.</div>
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