bims-chumac 2021-12-19 papers

Issue of 2021–12–19
two papers selected by
Thomas Krichel, Open Library Society

Perception. 2021 Dec 14. 3010066211065230

Impact of Face Masks and Viewers' Anxiety on Ratings of First Impressions from Faces.

Kun Guo, Alexander Hare, Chang Hong Liu.

Face mask is now a common feature in our social environment. Although face covering reduces our ability to recognize other's face identity and facial expressions, little is known about its impact on the formation of first impressions from faces. In two online experiments, we presented unfamiliar faces displaying neutral expressions with and without face masks, and participants rated the perceived approachableness, trustworthiness, attractiveness, and dominance from each face on a 9-point scale. Their anxiety levels were measured by the State-Trait Anxiety Inventory and Social Interaction Anxiety Scale. In comparison with mask-off condition, wearing face masks (mask-on) significantly increased the perceived approachableness and trustworthiness ratings, but showed little impact on increasing attractiveness or decreasing dominance ratings. Furthermore, both trait and state anxiety scores were negatively correlated with approachableness and trustworthiness ratings in both mask-off and mask-on conditions. Social anxiety scores, on the other hand, were negatively correlated with approachableness but not with trustworthiness ratings. It seems that the presence of a face mask can alter our first impressions of strangers. Although the ratings for approachableness, trustworthiness, attractiveness, and dominance were positively correlated, they appeared to be distinct constructs that were differentially influenced by face coverings and participants' anxiety types and levels.

Keywords: anxiety; approachableness; attractiveness; dominance; face covering; first impression; trustworthiness

DOI: https://doi.org/10.1177/03010066211065230

Demography. 2021 Dec 17. pii: 9648346. [Epub ahead of print]

What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany.

Bruno Arpino, Marco Le Moglie, Letizia Mencarini.

This study contributes to the literature on union dissolution by adopting a machine learning (ML) approach, specifically Random Survival Forests (RSF). We used RSF to analyze data on 2,038 married or cohabiting couples who participated in the German Socio-Economic Panel Survey, and found that RSF had considerably better predictive accuracy than conventional regression models. The man's and the woman's life satisfaction and the woman's percentage of housework were the most important predictors of union dissolution; several other variables (e.g., woman's working hours, being married) also showed substantial predictive power. RSF was able to detect complex patterns of association, and some predictors examined in previous studies showed marginal or null predictive power. Finally, while we found that some personality traits were strongly predictive of union dissolution, no interactions between those traits were evident, possibly reflecting assortative mating by personality traits. From a methodological point of view, the study demonstrates the potential benefits of ML techniques for the analysis of union dissolution and for demographic research in general. Key features of ML include the ability to handle a large number of predictors, the automatic detection of nonlinearities and nonadditivities between predictors and the outcome, generally superior predictive accuracy, and robustness against multicollinearity.

Keywords: Germany; Machine learning; Random Survival Forests; Socio-Economic Panel Study; Union dissolution

DOI: https://doi.org/10.1215/00703370-9648346