Red shape, blue shape: political ideology influences the social perception of body shape

The study investigates whether human body shape influences the attribution of political traits and whether observers’ political identification modulates these perceptions. Prior research shows that facial appearance affects voting behavior and trait inferences, and that political partisanship biases facial perceptions. However, little is known about how body shape influences political trait attribution. The authors hypothesize that, akin to faces, bodies elicit stereotypical political trait perceptions (e.g., Republican, Democrat, Liberal, Socialist, Aggressive), and that these perceptions vary with the rater’s political identity. Understanding these processes is important because rapid, automatic trait inferences from appearance can affect social outcomes including elections, employment, healthcare, and legal decisions.

Historical theories (Kretschmer; Sheldon’s somatotypes) attempted to link physique with character, though these have been discredited; nevertheless, body-shape stereotypes persist in social perception research. Modern work documents that appearance-based trait inferences are rapid and can predict election outcomes. Cognitive models propose that observers compare visual inputs to stereotypical mental representations of social categories. Data-driven methods and statistical face models have visualized mental representations for social traits (e.g., trustworthiness, dominance). For bodies, advances like the SMPL statistical 3D body model enable analogous investigation. Prior studies show body shape affects perceptions of emotion, identity, attractiveness, gender categorization, and personality; people can also infer personality traits from body shape. This literature motivates examining whether political trait attributions similarly map onto 3D body shape and are influenced by observers’ political identity.

Two online experiments were conducted with U.S. participants recruited via Amazon Mechanical Turk. Ethics approval was obtained and informed consent collected. Experiment 1: Associations between body shape and political traits - Political traits: 25 descriptors (including 'Republican' and 'Democrat' plus 23 associated terms gathered via a prior survey). Five body-shape terms were included as controls for data quality. - Participants: 492 (mean age 34.3±8.08 years; 227 male). Participants were split to rate either male or female bodies (male-body group n=245; female-body group n=247). Quality-control exclusions removed 66 raters from the male-bodies set and 75 from the female-bodies set based on catch trials. - Stimuli: 256 synthetic 3D bodies (128 male, 128 female) generated from the SMPL model using the first 8 principal shape components (accounting for 96.56% shape variance). Bodies were identically posed and rendered. - Procedure: Each participant rated 8 different bodies on 30 terms (25 political traits + 5 shape control terms), yielding 240 randomized body–term ratings per participant on a 5-point Likert scale. Two catch-trial bodies (one demonstrably big, one slim) with shape terms monitored attention/quality. A demographics questionnaire followed. - Modeling: For each gender, a linear regression was fit mapping averaged political trait ratings per body (25D vector) to the 8D SMPL shape coefficients. Leave-one-out cross-validation (LOOCV) predicted left-out bodies from ratings; reconstruction error (RE) was computed as mean absolute vertex distance (mm). Stereotype 3D bodies were visualized by conditioning on a target trait set to a high value and using learned correlations among traits to estimate the remaining ratings, then mapping to SMPL shape space. - Political–physical trait relations: Using Body Talk (Streuber et al., 2016) physical trait ratings, the authors computed covariance between political and physical trait ratings to explore semantic links. Experiment 2: Effect of political orientation on trait perception - Participants: 487 (mean age 34.7±7.97; 190 men); prior Experiment 1 participants were excluded. Quality exclusions removed 86 participants based on control ratings, resulting in the analyzed sample. - Stimuli: Four male bodies: SMPL average male; stereotype bodies for 'Democrat', 'Republican', and 'Trustworthy' generated from Experiment 1. - Descriptors: Six terms: 'Democrat', 'Republican', 'Trustworthy', 'Stubborn' (political/trait), and 'Heavy', 'Fit' (physical controls). - Procedure: Each participant rated the 4 bodies on the 6 descriptors (24 ratings total). Demographics followed, including political affiliation (Democrat, Republican, Independent, None, Other). For analyses of political identification, participants identifying as Democrat (n=159), Republican (n=75), or Independent (n=142) were grouped accordingly. - Analyses: Manipulation checks tested whether stereotype bodies elicited corresponding trait ratings (Bonferroni-corrected independent t-tests). Group differences (Democrat vs Republican raters) in ratings for each body–trait pair were tested with independent t-tests (Bonferroni-corrected). Potential confounds (height, weight, gender) were tested between groups.

Experiment 1 - Linear mapping between political trait ratings and 3D body shape: LOOCV reconstruction error (RE) was substantially lower than random mapping. - Male model: RE=15.41 mm (SD 7.05) vs random 27.94 mm (SD 13.04); t=11.38; p=4.49×10^-21. - Female model: RE=14.49 mm (SD 6.43) vs random 26.49 mm (SD 11.93); t=11.51; p=6.36×10^-25. These results support a reliable linear association allowing prediction of body shape from political trait ratings. - Stereotype 3D bodies: Distinct body shapes were visualized for each political descriptor for men and women, revealing perceived associations. - Political–physical trait links: Significant correlations emerged between political and physical trait ratings. Examples include: - Male 'Republican' stereotype associated with 'Heavyset', 'Stocky', 'Sturdy', and low 'Fit'. - Male 'Democrat' stereotype associated with 'Masculine', 'Lean', and 'Fit'. - Sex-specific differences: 'Republican' correlated positively with 'Tall' for male bodies but negatively for female bodies; 'Leader' correlated positively with 'Big' for male bodies and negatively for female bodies. Experiment 2 - Manipulation check: Stereotype bodies elicited significantly higher ratings on their corresponding descriptors than average ratings (Bonferroni-corrected t-tests), validating the generated stimuli. - Political identification effects: Ratings differed significantly by rater political affiliation (Democrat vs Republican) for specific body–trait pairs (Bonferroni-corrected). Illustrative statistics from independent t-tests: - For the Republican stereotype body, 'Republican' ratings were higher overall and differed by group (e.g., t=4.04; p=7.02×10^-5), with Democrat raters rating it as more 'Republican' than Republican raters. - For the Republican stereotype body, 'Democrat' ratings showed group differences (e.g., t=-4.32; p=2.23×10^-3). - For the Democrat stereotype body, 'Heavy' showed group differences (e.g., t=-2.75; p=0.006). - Trait-specific patterns across groups: The 'Republican' stereotype body was rated as less 'Trustworthy' and more 'Stubborn' regardless of rater political identity. - No confounds: No significant differences between Democrat and Republican raters in height (t=0.44, p=0.65), weight (t=1.29, p=0.19), or gender (t=−1.19, p=0.23). - Group-specific stereotype reconstructions: When models were fit separately by rater political identity, visualized stereotype bodies differed; notably, Republicans’ mental representation of 'Republican' was skinnier than Democrats’ representation, consistent with in-group favoritism effects. Overall, results show that observers infer political traits from body shape and that these inferences are systematically modulated by the observers’ political identity.

Findings demonstrate that body shape, like facial appearance, drives rapid social judgments about political traits. The successful prediction of 3D body shape from political trait ratings and the visualization of distinct stereotype bodies indicate that observers share mental representations linking physique to political categories. The discovered correlations between political and physical trait descriptors, and sex-specific differences, suggest complex stereotype structures that differ for male and female bodies. Experiment 2 confirms that political identification of observers modulates these perceptions: Democrat and Republican raters interpret the same bodies differently, often in ways consistent with in-group favoritism and out-group prejudice. Some traits (e.g., trustworthiness and stubbornness for the Republican stereotype body) were evaluated similarly across groups, indicating shared stereotypes alongside partisan modulation. These findings address the core research questions by evidencing both the existence of appearance-based political trait inferences from bodies and their sensitivity to the observer’s political identity. The results have implications for political behavior, suggesting that candidates’ body shapes may influence voter judgments, and more broadly for awareness of implicit biases affecting evaluations in societal domains (healthcare, legal contexts, education).

The study establishes a mathematical, linear relationship between 3D body shape and perceived political traits, and shows that observers’ political identity significantly modulates these perceptions. Using a statistical body model and perceptual ratings, the authors visualize stereotype bodies associated with political traits and reveal links between political and physical trait attributions, with notable sex-specific differences. These insights highlight potential impacts of body-based biases on electoral decisions and societal judgments and underscore the value of raising awareness to foster more informed decision-making. Potential future research directions include: extending analyses to more diverse populations and cultural contexts; examining dynamic body cues and posture; integrating facial and bodily information; exploring non-linear models and richer body shape parameters; and testing interventions to mitigate appearance-based bias in political and other decision-making contexts.