Patterns of correlation of facial shape with physiological measurements are more integrated than patterns of correlation with ratings

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.

Calibrating facial morphs for use as stimuli in biological studies of social perception

“…The original photographs of the adolescents were then unwarped to these target configurations and averaged. Apparent changes along this progression in small features (e.g., eyebrow arching) are epiphenomena of the underlying regression of shape on BFP, a pattern showing large-scale integration 11 . They are not experimentally controlled or otherwise manipulated features of the stimulus ordination per se.…”

“…The topic of BFP’s facial correlates is a suitable choice for demonstrating the method because facial correlates of BMI were the most integrated pattern 11 and at the same time contribute to current research into the social consequences of obesity or its opposite, which we refer to here as “leanness.” We modified the image unwarping and averaging previously explained in Windhager et al . 12 to yield a series of five facial configurations of female adolescents calibrated by BFP.…”

Social Perception of Faces: Brain Imaging and Subjective Ratings

“…We cannot rule out that distinctive small-scale facial features of different BFP image morphs may have influenced the behavioral and physiological responses in this study (cf. also Windhager et al, [45]), because neutral facial expressions also convey emotional meaning [46]. For example, the corners of the mouth are slightly downturned in the +5 SD BFP images (probably due to fatty pads or water retention), whereas they seem slightly raised in the −5 SD BFP images, which may have inadvertently elicited affective elements in the viewers [47][48][49].…”

“…The thicker facial morphs also feature smaller eyes and lower eyebrows, which could have potentially influenced the social perception of the face images we used in this study. Along these lines, Windhager et al [45] showed that raters overweighed small-scale variation in face shape when judging the health status in comparison to the global shape patterns associated with body mass index in male faces. Altogether, with the use of calibrated geometric morphometric morphs (for the statistical advantages, see Windhager et al, [34] in brain imaging, we hope bridging expertise of diverse disciplines might ramify into models of neural processing patterns, which can then be systematically tested over a variety of physical predictors in social perception, stereotyping and stigmatization from faces and bodies.…”

The Face of Early Cognitive Decline? Shape and Asymmetry Predict Choice Reaction Time Independent of Age, Diet or Exercise

“…Each PC ( Figure 2) accounted for 30.88, 19.33, 14.12 percent of the shape variance respectively. We included the face shape PCs in the analysis as data-driven face shape variation may be a better predictor of health and quality (e.g., attractiveness) than theory-driven measures such as facial symmetry [58,[71][72][73].…”