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Slobodan Markovic, Tara Bulut; Attractiveness of female and male body: comparison of subjective and objective measures. Journal of Vision 2017;17(10):1250. doi: https://doi.org/10.1167/17.10.1250.
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© ARVO (1962-2015); The Authors (2016-present)
The present study evaluated two hypotheses of human body attractiveness. The first is the Preference-for-average hypothesis: average female or male body shapes are most attractive, because they are "winning" evolutionary solutions for a certain population. The second is the Preference-for-supernormal hypothesis: according to the so-called "peak shift effect", the most attractive body is more masculine or feminine than the average, because it additionally stresses the crucial physical differences between genders. Forty-seven participants of both genders participated in the study. In the first part of the study we collected subjective measures: using a program for computer animation (DAZ 3D), participants created (1) most attractive and (2) subjectively estimated average female and male figures. These tasks were performed by adjusting the size of three body parts of both female and male figures: waist, hips and shoulders. Analyses showed no main effects of participants' gender for all subjective measures. The effect of task was significant for waist and shoulders: compared to average, most attractive female and male figures have broader shoulders and narrower waist. In the second part of the study we collected objective measures, i.e. participants' self-measures of waist, hips and shoulders circumference. Comparison of subjective and objective measures of waist-to-hip ratio (WHR) and shoulder-to-hip ratio (SHR) supported the Preference-for-supernormal hypothesis. Both female and male participants created figures which are generally more feminine (lower WHR) or masculine (higher SHR) than those created as average, and, interestingly, objective measures are less feminine or masculine than the ones subjectively estimated as average bodily proportions.
Meeting abstract presented at VSS 2017
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