Mapping physical characteristics in face images to social judgements

Sutherland and Young (Br. J. Psychol., 113, 2022, 1056) provide a comprehensive and timely overview of recent developments in research on social judgements of faces, emphasizing the utility of data-driven approaches. Here, we expand on this theme, focusing on how data-driven approaches can provide new insights into the physical characteristics in face images that best predict social judgements.     

We need to move beyond rating scales,white faces and adult perceivers: Invited Commentary on Sutherland & Young (2022), understanding trait impressions from faces

Sutherland and Young's perspective is a timely and rigorous examination of trait impressions based on facial cues. We propose three strtegies to further advance the field: incorporating natural language processing, including diverse facial stimuli, and re-interpreting developmental data.     

Participation,influence, and satisfaction in group decision making

The hypothesis that attitudinal effects of participation depend on individual differences in motivation was tested in a laboratory experiment with 56 three-man groups (leader and two members). Measures of the attractiveness of power and social acceptance were obtained prior to a group decision task, after which members described their perceived participation, influence, and satisfaction. Results showed that: (a) influence was more strongly related to satisfaction for members with strong, as opposed to weak, power motives; (b) for members with strong affiliation motives, participation was more strongly related to satisfaction than was influence. Relationships varied across satisfaction aspects. It was concluded that participation may be associated with favorable role attitudes through different motive-attainment mechanisms in the group decision-making process.     

A note on McDonald's generalization of principal components analysis

It is shown that McDonald's generalization of classical Principal Components Analysis to groups of variables maximally channels the total variance of the original variables through the groups of variables acting as groups. A useful equation is obtained for determining the vectors of correlations of theL2 components with the original variables. A calculation example is given.