Abstract

This article exploits a method recently incorporated in the geometric morphometric toolkit that complements previous approaches to quantifying the facial features associated with specific body characteristics and trait attribution during social perception. The new method differentiates more globally encoded from more locally encoded information by a summary scaling dimension that is estimated by fitting a line to the plot of log bending energy against log variance explained, partial warp by partial warp, for some sample of varying shapes. In the present context these variances come from the regressions of shape on some exogenous cause or effect of form. We work an example involving data from male faces. Here the regression slopes are steepest, and the sums of explained variances over the uniform component, partial warp 1 and partial warp 2 are greatest, for the conventional body mass index, followed by cortisol and, lastly, perceived health. This suggests that physiological characteristics may be represented at larger scale (global patterns), whereas cues in perception are of smaller scale (local patterns). Such a polarity within psychomorphospace, the global versus the focal, now has a metric by which patterns of morphology can be modeled in both biological and psychological studies.

Highlights

  • IntroductionAngles, or ratios, GMM is based on a complete multivariate analysis of the locations (that is, the Cartesian coordinates) of a designed set of landmark and semilandmark points taken all together

  • Instead of using distances, angles, or ratios, GMM is based on a complete multivariate analysis of the locations of a designed set of landmark and semilandmark points taken all together

  • Integration implies a large contribution from large-scale variation, whereas dis-integration can be interpreted as a higher amplitude of small-scale variation in facial signals

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Summary

Introduction

Angles, or ratios, GMM is based on a complete multivariate analysis of the locations (that is, the Cartesian coordinates) of a designed set of landmark and semilandmark points taken all together. Our article exemplifies the new analysis using regressions of male faces on three traits typically associated with facial shape: a physical trait (body mass index, BMI), an endocrinological measure (salivary cortisol), and a rating (perceived health). One cannot say whether it is the individual features (eyebrows, eyes, nose, mouth – all aspects of local variation) or instead general aspects of shape such as the overall shape of the facial outline or facial width-to-height ratio (global variation) that carry most of the signal Approaches to this puzzle included the dissection of the face into single features and their isolation and systematic variation via line drawings or identi-kits Our new approach may not rewrite these intuitive understandings, but it will quantify them for the first time

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