Lauren N. Berry

Background: Past studies demonstrate that certain facial features systematically affect first impressions of psychological traits. However, no previous studies have examined how individual differences in facial health affect first impressions of psychological traits. Methods: In this study, we asked a large sample of fairgoers to give their first impressions of psychological traits in response to viewing videos of unilateral facial paralysis patients with varying degrees of facial functioning. Then, we used linear mixed-effects regression models to understand how individual differences in facial health predict first impressions. Results: Our results replicate previous findings regarding first impressions of faces, such as the attractiveness halo effect, as well as age (maturity) and gender (masculinity) effects. More importantly, our results reveal that facial health, as measured by a clinician-graded scale, is a significant predictor of first impressions. Specifically, we found that individuals with better dynamic facial health (as assessed by clinicians) were perceived to be more competent and more affiliative, but not more dominant, than individuals with lower levels of dynamic facial functioning. Conclusions: Our results have important implications for personalized medicine via the development and refinement of individually tailored therapies to improve facial reanimation surgery outcomes.

Functional data analysis techniques, such as penalized splines, have become common tools used in a variety of applied research settings. Penalized spline estimators are frequently used in applied research to estimate unknown functions from noisy data. The success of these estimators depends on choosing a tuning parameter that provides the correct balance between fitting and smoothing the data. Several different smoothing parameter selection methods have been proposed for choosing a reasonable tuning parameter. The proposed methods generally fall into one of three categories: cross-validation methods, information theoretic methods, or maximum likelihood methods. Despite the well-known importance of selecting an ideal smoothing parameter, there is little agreement in the literature regarding which method(s) should be considered when analyzing real data. In this paper, we address this issue by exploring the practical performance of six popular tuning methods under a variety of simulated and real data situations. Our results reveal that maximum likelihood methods outperform the popular cross-validation methods in most situations—especially in the presence of correlated errors. Furthermore, our results reveal that the maximum likelihood methods perform well even when the errors are non-Gaussian and/or heteroscedastic. For real data applications, we recommend comparing results using cross-validation and maximum likelihood tuning methods, given that these methods tend to perform similarly (differently) when the model is correctly (incorrectly) specified.