Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others. However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry (...) or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method. (shrink)

Previous research has demonstrated how emotion resembling cues in the face help shape impression formation. Perhaps most notable in the literature to date, has been work suggesting that gender-related appearance cues are visually confounded with certain stereotypic expressive cues. Only a couple studies to date have used computer vision to directly map out and test facial structural resemblance to emotion expressions using facial landmark coordinates to estimate face shape. In one study using a Bayesian network classifier trained to detect emotional (...) expressions structural resemblance to a specific expression on a non-expressive face was found to influence trait impressions of others. In another study, a connectionist model trained to detect emotional expressions found different emotion-resembling cues in male vs. female faces. Despite this seminal work, direct evidence confirming the theoretical assertion that humans likewise utilize these emotion-resembling cues when forming impressions has been lacking. Across four studies, we replicate and extend these prior findings using new advances in computer vision to examine gender-related, emotion-resembling structure, color, and texture and their impact on gender-stereotypic impression formation. We show that all three are meaningfully related to human impressions of emotionally neutral faces. Further when applying the computer vision algorithms to experimentally manipulate faces, we show that humans derive similar impressions from them as did the computer. (shrink)

Social exclusion influences how expressions are perceived and the tendency of the perceiver to mimic them. However, less is known about social exclusion’s effect on one’s own facial expressions. The aim of the present study was to identify the effects of social exclusion on Duchenne smiling behaviour, defined as activity of both zygomaticus major and the orbicularis oculi muscles. Utilising a within-subject’s design, participants took part in the Cyberball Task in which they were both included and excluded while facial electromyography (...) was measured. We found that during the active experience of social exclusion, participants showed greater orbicularis oculi activation when compared to the social inclusion condition. Further, we found that across both conditions, participants showed greater zygomaticus major muscle activation the longer they engaged in the Cyberball Task. Order of condition also mattered, with those who experienced social exclusion before social inclusion showing the greatest overall muscle activation. These results are consistent with an affiliative function of smiling, particularly as social exclusion engaged activation of muscles associated with a Duchenne smile. (shrink)