Asymmetric visual representation of sex from human body shape

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We efficiently infer others’ states and traits from their appearance, and these inferences powerfully shape our social behaviour. One key trait is sex, which is strongly cued by the appearance of the body. What are the visual representations that link body shape to sex? Previous studies of visual sex judgment tasks find observers have a bias to report “male”, particularly for ambiguous stimuli. This finding implies a representational asymmetry – that for the processes that generate a sex percept, the default output is “male”, and “female” is determined by the presence of additional perceptual evidence. That is, female body shapes are positively coded by reference to a male default shape. This perspective makes a novel prediction in line with Treisman’s studies of visual search asymmetries: female body targets should be more readily detected amongst male distractors than vice versa. Across 10 experiments (N=32 each) we confirmed this prediction and ruled out alternative low-level explanations. The asymmetry was found with profile and frontal body silhouettes, frontal photographs, and schematised icons. Low-level confounds were controlled by balancing silhouette images for size and homogeneity, and by matching physical properties of photographs. The female advantage was nulled for inverted icons, but intact for inverted photographs, suggesting reliance on distinct cues to sex for different body depictions. Together, these findings demonstrate a principle of the perceptual coding that links bodily appearance with a significant social trait: the female body shape is coded as an extension of a male default. We conclude by offering a visual experience account of how these asymmetric representations arise in the first place.

Keywords

  • social vision, body perception, sex perception, visual search, search asymmetry, visual experience
Original languageEnglish
Article number104436
JournalCognition
Volume205
Early online date9 Sept 2020
DOIs
Publication statusPublished - Dec 2020

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