Modeling individual preferences reveals that face beauty is not universally perceived across cultures

Zhan, J., Liu, M., Garrod, O. G.B., Daube, C., Ince, R. A.A. , Jack, R. E. and Schyns, P. G. (2021) Modeling individual preferences reveals that face beauty is not universally perceived across cultures. Current Biology, 31(10), 2243-2252.e6. (doi: 10.1016/j.cub.2021.03.013) (PMID:33798430) (PMCID:PMC8162177)

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Abstract

Facial attractiveness confers considerable advantages in social interactions,1,2 with preferences likely reflecting psychobiological mechanisms shaped by natural selection. Theories of universal beauty propose that attractive faces comprise features that are closer to the population average3 while optimizing sexual dimorphism.4 However, emerging evidence questions this model as an accurate representation of facial attractiveness,5, 6, 7 including representing the diversity of beauty preferences within and across cultures.8, 9, 10, 11, 12 Here, we demonstrate that Western Europeans (WEs) and East Asians (EAs) evaluate facial beauty using culture-specific features, contradicting theories of universality. With a data-driven method, we modeled, at both the individual and group levels, the attractive face features of young females (25 years old) in two matched groups each of 40 young male WE and EA participants. Specifically, we generated a broad range of same- and other-ethnicity female faces with naturally varying shapes and complexions. Participants rated each on attractiveness. We then reverse correlated the face features that drive perception of attractiveness in each participant. From these individual face models, we reconstructed a facial attractiveness representation space that explains preference variations. We show that facial attractiveness is distinct both from averageness and from sexual dimorphism in both cultures. Finally, we disentangled attractive face features into those shared across cultures, culture specific, and specific to individual participants, thereby revealing their diversity. Our results have direct theoretical and methodological impact for representing diversity in social perception and for the design of culturally and ethnically sensitive socially interactive digital agents.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jack, Professor Rachael and Daube, Mr Christoph and Garrod, Dr Oliver and Schyns, Professor Philippe and Zhan, Dr Jiayu and Ince, Dr Robin and Liu, Meng
Authors: Zhan, J., Liu, M., Garrod, O. G.B., Daube, C., Ince, R. A.A., Jack, R. E., and Schyns, P. G.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
College of Science and Engineering > School of Psychology
Journal Name:Current Biology
Publisher:Elsevier (Cell Press)
ISSN:0960-9822
ISSN (Online):1879-0445
Published Online:01 April 2021
Copyright Holders:Copyright © 2021 Crown Copyright
First Published:First published in Current Biology 31(10): 2243-2252.e6
Publisher Policy:Reproduced under a Creative Commons License
Related URLs:
Data DOI:10.17632/cvh2d2bz6r.2

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172413Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time seriesPhilippe SchynsWellcome Trust (WELLCOTR)107802/Z/15/ZNP - Centre for Cognitive Neuroimaging (CCNi)
172046Visual Commonsense for Scene UnderstandingPhilippe SchynsEngineering and Physical Sciences Research Council (EPSRC)EP/N019261/1NP - Centre for Cognitive Neuroimaging (CCNi)