Perception of Sexual Orientation from Facial Structure: A Study with Artificial Face Models

Abstract

Research has shown that lay people can perceive sexual orientation better than chance from face stimuli. However, the relation between facial structure and sexual orientation has been scarcely examined. Recently, an extensive morphometric study on a large sample of Canadian people (Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015) identified three (in men) and four (in women) facial features as unique multivariate predictors of sexual orientation in each sex group. The present study tested the perceptual validity of these facial traits with two experiments based on realistic artificial 3D face models created by manipulating the key parameters and presented to Spanish participants. Experiment 1 included 200 White and Black face models of both sexes. The results showed an overall accuracy (0.74) clearly above chance in a binary hetero/homosexual judgment task and significant differences depending on the race and sex of the face models. Experiment 2 produced five versions of 24 artificial faces of both sexes varying the key parameters in equal steps, and participants had to rate on a 1-7 scale how likely they thought that the depicted person had a homosexual sexual orientation. Rating scores displayed an almost perfect linear regression as a function of the parameter steps. In summary, both experiments demonstrated the perceptual validity of the seven multivariate predictors identified by Skorska et al. and open up new avenues for further research on this issue with artificial face models.

Emotion and Gender Typicality Cue Sexual Orientation Differently in Women and Men

“…Thus, gendered cues facilitate detection of sexual orientation, establishing gender typicality as a valid (albeit imperfect) cue to sexual orientation cue (e.g., Freeman et al, 2010 ; Munson & Babel, 2007 ). Importantly, whereas some of these gender typicality differences may stem from biological differences (e.g., facial morphology; González-Álvarez, 2017 ; Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015 ; gait due to sexual dimorphism; Cutting, 1978 ), others rely on self-presentation (e.g., hairstyle; Krakauer & Rose, 2002 ; Rule et al, 2008 ; gait due to learned gender roles; see Johnson et al, 2007 ).…”

Speech Acoustic Features: A Comparison of Gay Men, Heterosexual Men, and Heterosexual Women

“…The gender atypicality hypothesis suggests that gender atypical traits in homosexuals could be used as cues to indicate sexual orientation. Differences between heterosexual and homosexual individuals have thus been studied on a diverse set of traits such as face (e.g., Freeman, Johnson, Ambady, & Rule, 2010 ; González-Álvarez, 2017 ; Lyons, Lynch, Brewer, & Bruno, 2014 ; Rieger, Linsenmeier, Gygax, Garcia, & Bailey, 2010 ; Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015 ; Wang & Kosinski, 2018 ), olfaction (e.g., Sergeant, Dickins, Davies, & Griffiths, 2007 ), behavior (e.g., Ambady, Hallahan, & Conner, 1999 ; Rieger, Linsenmeier, Gygax, & Bailey, 2008 ; Valentova, Rieger, Havlicek, Linsenmeier, & Bailey, 2011 ), cognition (e.g., Neave, Menaged, & Weightman, 1999 ; Xu, Norton, & Rahman, 2017 ), and voice (e.g., Gaudio, 1994 ; Munson, McDonald, DeBoe, & White, 2006b ; Pierrehumbert, Bent, Munson, Bradlow, & Bailey, 2004 ; Rendall, Vasey, & McKenzie, 2008 ). In addition to the fact that homosexuals exhibit traits that differ from those of heterosexuals, it has been shown that some of them, such as specific neural processes (LeVay, 1991 ; Savic, Berglund, & Lindstrom, 2005 ) or specific childhood behaviors (Alanko et al, 2010 ; Bailey & Zucker, 1995 ), displayed values shifted toward those of the opposite sex, i.e., a feminization in homosexual men and a masculinization in homosexual women (Pierrehumbert et al, 2004 ).…”

The role of facial skin tone and texture in the perception of age

“…FaceGen was based on a face space created by PCA (Principal Components Analysis) from a dataset comprising 273 human faces that were laser-scanned in high-resolution 3D (Blanz & Vetter, 1999). This software contains age, sex and racial controls based on linear regressions on the dataset in that face space, and it has been widely used in research on face perception in high-impact publications (e.g., Cenac, Biotti, Gray, & Cook, 2019;González-Alvarez, 2017;González-Álvarez & Sos-Peña, 2022;Kihara & Takeda, 2019;Oh, Dotsch, & Todorov, 2019;Oosterhof & Todorov, 2008;Qiu & Mei, 2021;Todorov, Pakrashi, & Oosterhof, 2009).…”