Literature DB >> 35230674

A multilevel Bayesian meta-analysis of the body inversion effect: Evaluating controversies over headless and sexualized bodies.

Jason W Griffin1, Flora Oswald2,3.   

Abstract

Face and body perception rely on specialized processing mechanisms to interpret social information efficiently. The body inversion effect (BIE), refers to an inversion effect for bodies, such that recognition of bodies is impaired by inversion. The BIE, like the face inversion effect (FIE), is particularly important because a disproportionate BIE relative to inversion effects for objects could be interpreted in much the same way as the disproportionate FIE has often been characterized; that is, as evidence of specialized, configural processing. However, research supporting the BIE is marked by methodological heterogeneity and mixed findings. Our multilevel Bayesian meta-analysis addresses inconsistencies in the literature by pooling data from numerous studies to estimate the magnitude of the BIE across various methodological and stimulus properties. We included 180 effect sizes from 41 empirical articles representing data from 2,274 participants. Overall, we found that the BIE was moderate-large in magnitude (Hedges' g = 0.75). Importantly, the inversion effect was larger for bodies than objects (b = 0.42); however, the inversion effect for faces was larger than for bodies (b = 0.34). We tested the role of discrimination dimension, stimulus type, face/head inclusion, stimulus sexualization, and sexualized stimulus sex as moderators of the BIE. We found that the BIE was moderated by discrimination dimension, stimulus type, stimulus sexualization, and sexualized stimulus sex. By synthesizing the existing literature, we provide a better theoretical understanding of how underlying visual processing mechanisms may differ for different types of social information (i.e., bodies vs. faces).
© 2022. The Psychonomic Society, Inc.

Entities:  

Keywords:  Body perception; Face inversion; Inverted; Objectification; Recognition; Sexualized body inversion hypothesis; Upright

Mesh:

Year:  2022        PMID: 35230674     DOI: 10.3758/s13423-022-02067-3

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  35 in total

1.  Becoming a face expert.

Authors:  S Carey
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  1992-01-29       Impact factor: 6.237

2.  Configural face processing: a meta-analytic survey.

Authors:  Raymond Bruyer
Journal:  Perception       Date:  2011       Impact factor: 1.490

3.  Remembering facial configurations.

Authors:  V Bruce; T Doyle; N Dench; M Burton
Journal:  Cognition       Date:  1991-02

4.  Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach.

Authors:  Mike W-L Cheung
Journal:  Psychol Methods       Date:  2013-07-08

5.  Perceptual determinants are critical, but they don't explain everything: a response to Tarr (2013).

Authors:  Philippe Bernard; Sarah J Gervais; Jill Allen; Olivier Klein
Journal:  Psychol Sci       Date:  2013-04-18

6.  The influence of personal BMI on body size estimations and sensitivity to body size change in anorexia spectrum disorders.

Authors:  Katri K Cornelissen; Andre Bester; Paul Cairns; Martin J Tovée; Piers L Cornelissen
Journal:  Body Image       Date:  2015-02-17

7.  A visual short-term memory advantage for objects of expertise.

Authors:  Kim M Curby; Kuba Glazek; Isabel Gauthier
Journal:  J Exp Psychol Hum Percept Perform       Date:  2009-02       Impact factor: 3.332

8.  Where You Look Matters for Body Perception: Preferred Gaze Location Contributes to the Body Inversion Effect.

Authors:  Joseph M Arizpe; Danielle L McKean; Jack W Tsao; Annie W-Y Chan
Journal:  PLoS One       Date:  2017-01-13       Impact factor: 3.240

9.  An Assessment of Computer-Generated Stimuli for Use in Studies of Body Size Estimation and Bias.

Authors:  Joanna Alexi; Kendra Dommisse; Dominique Cleary; Romina Palermo; Nadine Kloth; Jason Bell
Journal:  Front Psychol       Date:  2019-10-22

10.  How Well Do Computer-Generated Faces Tap Face Expertise?

Authors:  Kate Crookes; Louise Ewing; Ju-Dith Gildenhuys; Nadine Kloth; William G Hayward; Matt Oxner; Stephen Pond; Gillian Rhodes
Journal:  PLoS One       Date:  2015-11-04       Impact factor: 3.240

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