Literature DB >> 31734634

Contributions of shape and reflectance information to social judgments from faces.

DongWon Oh1, Ron Dotsch2, Alexander Todorov3.   

Abstract

Face perception is based on both shape and reflectance information. However, we know little about the relative contribution of these kinds of information to social judgments of faces. In Experiment 1, we generated faces using validated computational models of attractiveness, competence, dominance, extroversion, and trustworthiness. Faces were manipulated orthogonally on five levels of shape and reflectance for each model. Both kinds of information had linear and additive effects on participants' social judgments. Shape information was more predictive of dominance, extroversion, and trustworthiness judgments, whereas reflectance information was more predictive of competence judgments. In Experiment 2, to test whether the amount of visual information alters the relative contribution of shape and reflectance information, we presented faces - varied on attractiveness, competence, and dominance - for five different durations (33-500 ms). For all judgments, the linear effect of both shape and reflectance increased as duration increased. Importantly, the relative contribution did not change across durations. These findings show that that the judged dimension is critical for which kind of information is weighted more heavily in judgments and that the relative contribution of shape and reflectance is stable across the amount of visual information available.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Computational models; Face perception; Social cognition; Social perception

Mesh:

Year:  2019        PMID: 31734634     DOI: 10.1016/j.visres.2019.10.010

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  2 in total

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Authors:  Terence J McElvaney; Magda Osman; Isabelle Mareschal
Journal:  Mem Cognit       Date:  2022-01-13

2.  Unsupervised inference approach to facial attractiveness.

Authors:  Miguel Ibanez-Berganza; Ambra Amico; Gian Luca Lancia; Federico Maggiore; Bernardo Monechi; Vittorio Loreto
Journal:  PeerJ       Date:  2020-10-28       Impact factor: 2.984

  2 in total

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