Bayesian Advice for Gaydar-based Picking Up: Commentary on Lyons, Lynch, Brewer, and Bruno (2013)

Abstract

Consider the following: About 5 % of people are homosexual. Is it possible to detect from appearance if somebody is homosexual? This seems to be possible. In a recent study, participants were able to detect if somebody was homosexual better than chance. In this study, participants had to judge the sexual orientation of individuals based on 80 photographs of faces: 40 photographs depicted a homosexual person; the other 40 photographs depicted a heterosexual person. It turned out that 70 % of the homosexual persons were correctly identified. Of the heterosexual pictures, only 20 % were incorrectly classified as homosexual. Now the question: Assume that all individuals can be as accurate in detecting sexual orientation as in this study. If one sees a randomly chosen individual in the street and judges the person to be homosexual, what is the chance (between 0 and 100 %) that the person is indeed homosexual? You may be surprised that the correct answer is a quite low probability: 16 %. The problem described above is based on a recent article by Lyons, Lynch, Brewer, and Bruno (2013) who reported experimental research about women’s accuracy of judging the sexual orientation of individuals based on photographs. Theparticipatingwomencouldcorrectly identifygay/lesbianand heterosexual targets better than chance, i.e., the correct identification of gay/lesbian targets ranged between 58–65 %, and only 16–37 % of the heterosexuals were falsely identified as gay/lesbian. Thisadds toanumberofother innovativeexperimental‘‘gaydar’’studies with comparable accuracy (e.g., Ambady, Hallahan, & Conner, 1999; Johnson, Gill, Reichman, & Tassinary, 2007; Rieger, Linsenmeyer, Gygax, Garcia, & Bailey, 2009). But how does gaydar work outside of the laboratory? In the Lyons et al. study, accuracy of judgment was estimated by calculating the hit rate: the number of correctly identified homosexual targets (true positives) divided by the number of all presented homosexual targets. In addition, the false alarm rate was calculated by the number ofheterosexual targetswho were falsely categorized as gay/lesbian (false alarms or false positives) divided by all presented heterosexual targets. When we want to translate the study findings into the real world, we have to consider the base-rate of gays/lesbians within the population, which is roughly at about 5 % and thus much less than in laboratory experiments where usually 50 % of the targets are gay/lesbian. By applying Bayes’ Theorem, we can now calculate theprobabilityof interest tousor tothosewhowant topick up potential same-sex partners from, let us say, a shopping-mall random-like sample; in other words, the probability that an individual is gay/lesbian, given our gaydar alarm rings.