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Data from: A new data-driven mathematical model dissociates attractiveness from sexual dimorphism of human faces

Citation

Nakamura, Koyo (2022), Data from: A new data-driven mathematical model dissociates attractiveness from sexual dimorphism of human faces, Dryad, Dataset, https://doi.org/10.5061/dryad.pg4f4qrmp

Abstract

Human facial attractiveness is evaluated by using multiple cues. Among others, sexual dimorphism (i.e. masculinity for male faces/femininity for female faces) is an influential factor of perceived attractiveness. Since facial attractiveness is judged by incorporating sexually dimorphic traits as well as other cues, it is theoretically possible to dissociate sexual dimorphism from facial attractiveness. This study tested this by using a data-driven mathematical modelling approach. We first analysed the correlation between perceived masculinity/femininity and attractiveness ratings for 400 computer-generated male and female faces (Experiment 1) and found positive correlations between perceived femininity and attractiveness for both male and female faces. Using these results, we manipulated a set of faces along the attractiveness dimension while controlling for sexual dimorphism by orthogonalisation with data-driven mathematical models (Experiment 2). Our results revealed that perceived attractiveness and sexual dimorphism are dissociable, suggesting that there are as yet unidentified facial cues other than sexual dimorphism that contribute to facial attractiveness. Future studies can investigate the true preference of sexual dimorphism or the genuine effects of attractiveness by using well-controlled facial stimuli like those that this study generated. The findings will be of benefit to the further understanding of what makes a face attractive.

Funding

Japan Society for the Promotion of Science, Award: 17J04125

Japan Society for the Promotion of Science, Award: 19K20387

Japan Society for the Promotion of Science, Award: 17H06344