A Perceptual Space for Describing Human Bodies Matthew Q. Hill, Carina A. Hahn, Alice J. O’Toole The University of Texas at Dallas Results Conclusions Interpretation height height 3 female shapes vs. “other” male shapes vs. “other” 4 masculine vs. curvy waist height 5 waist height toned vs. average Background Contributing descriptor terms • Adaptation and Norm-based Coding Studies of Body Perception • identity aftereffects within two bodies (Rhodes, Jeffery, Boeing, & Calder, 2013) • weight & gender adaptation invariant for viewpoint and pose Female Bodies: Axes 1-‐4 AXIS • virtual bodies from space of 2000 laser scans (Sekunova, et al., 2013) axis 2 tall, long, long legs skinny, round (apple), lean, peDte, small, ﬁt axis 3 pear-‐shaped, curvy masculine, rectangular, average, broad shoulders, muscular, long torso, short legs axis 4 curvy long torso, pear-‐shaped, short legs axis 5 body size terms • big, small, short, tall, heavyset, stocky, skinny, peDte global shape terms • round (apple), rectangular, long, pear-‐shaped, curvy ﬁtness terms • lean, ﬁt, muscular, built, sturdy local feature terms • long legs, short legs, long torso, short torso, broad shoulders averageness terms gender terms • masculine, feminine More heavy More ‘other’ “’Female shapes vs. ‘other’” More female skinny, lean, ﬁt short, average, short legs, small, feminine, short torso axis 2 tall, big, ﬁt, muscular skinny, long, small, long legs axis 3 muscular, built, ﬁt long torso, short legs, skinny, average axis 4 short torso, long legs ﬁt, muscular, small, built, lean, skinny, short torso axis 5 average, tall, long Lower waist axis 1 Participants • 12 undergraduate students (6 female) Procedure • each participant gave open ended ratings of bodies used in main experiment • use space to find real “opposite” bodies (in progress) “Waist height” heavyset, round (apple), big, stocky Method Rating scale 1. does not apply 2. applies somewhat 3. applies perfectly • test perceptual validity of body space using adaptation paradigm AXIS Feature Term Pilot Study Body Feature Descriptors Future References Higher waist • 2 images per identity: one standing, one walking (448 images total) • blurred to obscure facial identity Procedure • each participant rated 75 identities on 27 feature descriptors • total 2,025 judgments short torso, long legs “Weight” • rating data can be used to generate verbal descriptions when physical measurements are known Component 3: 10.78% Component 4: 7.43% Harms, Snow, Hurst, Pappas, & Abdi, 2005) • multivariate technique similar to principal component analysis • used for categorical rather than continuous variables • visualization of cross-tabular data • simultaneous visualization of observations and variables Component 1: 38.41% More skinny • space can be applied to generate similarity measures from verbal descriptions of bodies Male Bodies: Axes 1-‐4 Correspondence Analysis Participants • 60 undergraduate students (30 male) Stimuli • 224 identities: 164 female, 60 male (O’Toole, • average, proporDoned short, short legs, small, peDte, pear-‐shaped Shorter Method skinny, lean, proporDoned “Height” • participants rate the applicability of body descriptor terms to a large number of bodies • correspondence analysis (Greenacre, 2010) separately to male and female body descriptions • shared perceptual body and feature descriptor space: male and female bodies • enables visualization of feature terms and bodies in common space axis 1 Taller Approach big, heavyset, round (apple), stocky Component 2: 14.59% • silhouettes of bodies yield gender adaptation aftereffects (Palumbo, Laeng, & Tommasi, 2013) • rectangle width adaptation does not explain weight adaptation (Hummel, et al., 2012) More curvy 2 • common and gender-specific components for male and female body spaces Taller weight “Masculine vs. Curvy” • map body descriptions onto perceptual and physical body spaces weight More masculine • (e.g., from laser scans of bodies, Freifeld & Black, 2012) 1 • resulting spaces interpretable in the context of the feature terms Component 4: 6.73% • relate perceptual body spaces to physical body spaces Male “Height” Long Term Goals Female Shorter • create multidimensional representation of human body shapes based on perceptual judgments • map shape variation across individual bodies using body feature descriptors • represent both bodies and body descriptor terms in a common multidimensional space Axis Component 2: 12.02% Goals • possible to reverse engineer a body similarity space from body feature descriptors females • people commonly describe bodies using descriptors (e.g. skinny, curvy, heavyset, stocky, fit, muscular, built, petite) – Can descriptions be used to reverse engineer a representational space to describe body similarities? males Problem Feature selection Exploratory Analysis • categories: • size, global shape, fitness, local feature, averageness, gender • descriptor terms chosen based on: • frequency of use within each category Component 1: 31.88% More skinny “Weight” More heavy Component 3: 13.46% More male “Male shapes vs. More ‘other’ ‘other’” Freifeld, O., & Black, M. 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