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

Holzleitner, Iris J. and Lee, Anthony J. and Hahn, Amanda C. and Kandrik, Michal and Bovet, Jeanne and Renoult, Julien P. and Simmons, David and Garrod, Oliver and DeBruine, Lisa M. and Jones, Benedict C. (2019) Comparing theory-driven and data-driven attractiveness models using images of real women's faces. Journal of Experimental Psychology: Human Perception and Performance, 45 (12). pp. 1589-1595. ISSN 0096-1523

[img]
Preview
Text (Holzleitner-etal-JEPHPP-2019-Comparing-theory-driven-and-data-driven-attractiveness-models)
Holzleitner_etal_JEPHPP_2019_Comparing_theory_driven_and_data_driven_attractiveness_models.pdf
Accepted Author Manuscript

Download (2MB)| Preview

    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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).