Comparing theory-driven and data-driven attractiveness models using images of real women's faces

Holzleitner, Iris J.; Lee, Anthony J.; Hahn, Amanda C.; Kandrik, Michal; Bovet, Jeanne; Renoult, Julien P.; Simmons, David; Garrod, Oliver; DeBruine, Lisa M.; Jones, Benedict C.

Abstract

Facial attractiveness plays a critical role in social interaction, influencing many different social outcomes. However, the factors that influence facial attractiveness judgments remain relatively poorly understood. Here, we used a sample of 594 young adult female face images to compare the performance of existing theory-driven models of facial attractiveness and a data-driven (i.e., theory-neutral) model. Our data-driven model and a theory-driven model including various traits commonly studied in facial attractiveness research (asymmetry, averageness, sexual dimorphism, body mass index, and representational sparseness) performed similarly well. By contrast, univariate theory-driven models performed relatively poorly. These results (a) highlight the utility of data driven models of facial attractiveness and (b) suggest that theory-driven research on facial attractiveness would benefit from greater adoption of multivariate approaches, rather than the univariate approaches that they currently almost exclusively employ.

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2019
• Publication Date: Dec 1, 2019
• Deposit Date: Apr 28, 2021
• Journal: Journal of Experimental Psychology: Human Perception and Performance
• Print ISSN: 0096-1523
• Electronic ISSN: 1939-1277
• Publisher: American Psychological Association
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 12
• Pages: 1589-1595
• DOI: https://doi.org/10.1037/xhp0000685
• Keywords: Experimental and Cognitive Psychology; Arts and Humanities (miscellaneous); Behavioral Neuroscience
• Public URL: https://uwe-repository.worktribe.com/output/7309594
• Additional Information: Preprint at https://psyarxiv.com/vhc5k/; data and code available at https://osf.io/jurcq/