How Much Does Symmetry Influence Attractiveness Ratings?
- visible asymmetries are more important to attractiveness ratings than are non visible asymmetries F1,37=7.55 (p=.01)
- funnel plot analyses indicate a substantial publication bias in the literature
- studies with large sample sizes show a near zero relationship between attractiveness ratings and asymmetry F1,36=6.97 (p=.01)
Visible vs Not Visible Asymmetries
The distinction here is straightforward: if raters can see the measured asymmetry it is assumed that the asymmetry is visible. For example, if facial asymmetries are measured and faces are evaluated for attractiveness, then the study is categorized as visible. On the other hand, if the asymmetry is in the body and photos of faces are rated, the study is categorized as not visible. This visible/not visible distinction is relevant to why humans find symmetry attractive – for example in comparing a good genes interpretation of symmetry to a processing fluency interpretation. Van Dongen makes the point that the models underlying the good genes interpretation of the role of symmetry on attractiveness require that non-observable symmetry be significantly related to attractiveness ratings.
Since the value of a comprehensive data analysis is only as good as the data it uses, a check on the quality of the included sources is highly desirable. One such check is a funnel plot. In a funnel plot, each study is plotted for effect size and sample size. Since variation resulting from chance is more likely to be larger in studies with small sample sizes, a visual inspection of the plotted data points for the included studies should show a symmetric distribution around the typical effect size, with more variation in effect size expected for studies using smaller samples (hence the name, funnel plot). The published studies exploring the relationship between attractiveness and symmetry do not reveal the expected funnel-like symmetric shape. While a number of causes for this undesirable result are possible, publication bias is the most likely. A statistical technique used to minimize the effect of publication bias is the trim and fill method (especially useful in cases of publication bias). Van Dongen uses this technique to more accurately estimate the actual effect size of symmetry on beauty ratings.
A meta analysis that shows a decreasing effect size in studies with increasing sample sizes is another indicator of publication bias. The rationale for this conclusion is that there is a preference for publishing research that has found support for a particular hypothesis over research that reports finding no relationship. A manuscript that supports the null hypothesis is typically more interesting when the sample size is larger and thus gets published. Manuscripts that do not show a relationship are left ‘in the file drawer.’
Van Dongen’s overall study results, after accounting for bias, found that there was a significant effect of visible asymmetry on visual attractiveness ratings (r=.15 with a 95% confidence interval of 0.07-0.23). This degree of effect size is typically categorized as small/medium. What that means is that a person of average attractiveness (left side of the highlighted area below – the 50th percentile) who suddenly became more symmetric (by the typical amount of variation in symmetry found in human faces naturally) would now be rated more attractive than 62 percent of other people (the right side of he highlighted area below).
Another way to illustrate this degree of change is via a beauty rating scale. For ease of estimation, imagine an 8-point rating scale (from 0-8) that is normally distributed with 4 as the average, typical score. This degree of change would take the average person’s rating from a 4 to a 4.3. On this same scale, a person who is in all other respects rated average for attractiveness, but who had an exceptionally high degree of symmetry, would likely be rated a 5 rather than a 4 (this degree of symmetry is expected only in about 1/1000 people. It is important to note that for the illustrations above to hold, the changes in symmetry must be visible. Van Dongen’s meta analysis found no relationship between attractiveness ratings and the symmetry measures of features that are not visible to the person doing the rating.
One significant limitation in the symmetry/attractiveness literature is that the data primarily come from western, college student samples – limiting our ability to generalize these conclusions to other populations.
Van Dongen, S. (2011). Associations between asymmetry and human attractiveness: Possible direct effects of asymmetry and signatures of publication bias Annals of Human Biology, 38 (3), 317-323 DOI: 10.3109/03014460.2010.544676