Physical strength as a cue to dominance: A data-driven approach

Item does not contain fulltextWe investigate both similarities and differences between dominance and strength judgments using a data-driven approach. First, we created statistical face shape models of judgments of both dominance and physical strength. The resulting faces representing dominance and strength were highly similar, and participants were at chance in discriminating faces generated by the two models. Second, although the models are highly correlated, it is possible to create a model that captures their differences. This model generates faces that vary from dominant-yet-physically weak to nondominant-yet-physically strong. Participants were able to identify the difference in strength between the physically strong-yet-nondominant faces and the physically weak-yet-dominant faces. However, this was not the case for identifying dominance. These results suggest that representations of social dominance and physical strength are highly similar, and that strength is used as a cue for dominance more than dominance is used as a cue for strength.14 p

Validation of data-driven computational models of social perception of faces

People rapidly form impressions from facial appearance, and these impressions affect social decisions. We argue that data-driven, computational models are the best available tools for identifying the source of such impressions. Here we validate seven computational models of social judgments of faces: attractiveness, competence, dominance, extroversion, likability, threat, and trustworthiness. The models manipulate both face shape and reflectance (i.e., cues such as pigmentation and skin smoothness). We show that human judgments track the models’ predictions (Experiment 1) and that the models differentiate between different judgments, though this differentiation is constrained by the similarity of the models (Experiment 2). We also make the validated stimuli available for academic research: seven databases containing 25 identities manipulated in the respective model to take on seven different dimension values, ranging from −3 SD to +3 SD (175 stimuli in each database). Finally, we show how the computational models can be used to control for shared variance of the models. For example, even for highly correlated dimensions (e.g., dominance and threat), we can identify cues specific to each dimension and, consequently, generate faces that vary only on these cues