Holzleitner IJ, Lee A, Hahn AC, Kandrik M, Bovet J, Renoult JP, Simmons D, Garrod O, DeBruine LM & Jones BC (preprint). Comparing theory-driven and data-driven attractiveness models using images of real women’s faces. PsyArXiv . doi: 10.31234/osf.io/vhc5k [data]

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 predictive utility of existing theory-driven models of facial attractiveness and a data-driven (i.e., theory-neutral) model. Our data-driven model reliably explained significantly more variance in attractiveness than did theory-driven models including various different combinations of traits commonly studied in facial attractiveness research (asymmetry, averageness, sexual dimorphism, body mass index, and representational sparseness). These results present important new evidence for the utility of data-driven approaches to studying facial attractiveness, highlight the limitations of current theory-driven approaches, and indicate the importance of considering multivariate, rather than univariate, models when investigating facial attractiveness.

Disclaimer: The information found and the views expressed in these homepages are not the responsibility of the University of Glasgow nor do they reflect institutional policy.