Individualized models of social judgments and context-dependent representations

How individuals view the world is critical to understanding human behavior. Yet, almost all research within perception and judgment has drawn inferences from group-level behavior, with little work focused on understanding how the individual perceives their world. However, for complex judgments (e.g., trustworthiness), most of the meaningful variance is due to factors specific to the individual. Here we showcase a data-driven reverse correlation method for visualizing any perceptually-derived stereotype at the individual level. We show that our method (1) produces photorealistic and reliable results related to a broad range of judgments, (2) produces valid, psychologically-aligned representations of what individuals are imagining “in their mind’s eye”, and (3) is capable of capturing visual representations sensitive enough to examine context-dependent categories (e.g., a trustworthy individual to babysit your children vs. to fix your car). Across all studies, we highlight the theoretical implications and utility of developing idiosyncratic models of visual perception

Face evaluation: Findings, methods, and challenges

Complex evaluative judgments from facial appearance are made efficiently and are consequential. We review some of the most important findings and methods over the last two decades of research on face evaluation. Such evaluative judgments emerge early in development and show a surprising consistency over time and across cultures. Judgments of trustworthiness, in particular, are closely associated with general valence evaluation of faces and are grounded in resemblance to emotional expressions, signaling approach versus avoidance behaviors. Data-driven computational models have been critical for the discovery of the configurations of features, including resemblance to emotional expressions, driving specific judgments. However, almost all models are based on judgments aggregated across individuals, essentially masking idiosyncratic differences in judgments. Yet, recent research shows that most of the meaningful variance of complex judgments such as trustworthiness is idiosyncratic: explained not by stimulus features, but by participants and participants by stimuli interactions. Hence, to understand complex judgments, we need to develop methods for building models of judgments of individual participants. We describe one such method, combining the strengths of well-established methods with recent developments in machine learning

Capturing variability in children’s faces: an artificial, yet realistic, face stimulus set

Children’s faces are underrepresented in face databases, and existing databases that do focus on children tend to have limitations in terms of the number of faces available and the diversity of ages and ethnicities represented. To improve the availability of children’s faces for experimental research purposes, we created a novel face database that contains 500 artificial images of children that are diverse in terms of both age (ages 3 to 10) and ethnicity (representing 15 different racial or ethnic groups). Using deep neural networks, we produced a large collection of synthetic photographs that look like naturalistic, realistic faces of children. To assess the representativeness of the dataset, adult participants (N = 585) judged the age, gender, ethnicity, and emotion of artificial faces selected from the set of 500 images. The images present a diverse array of artificial children’s faces, offering a valuable resource for research requiring children’s faces. The images and ratings are publicly available to researchers on Open Science Framework (https://osf.io/m78r4/)

Culture Moderates the Relationship Between Emotional Fit and Collective Aspects of Well-Being

The present study examined how emotional fit with culture – the degree of similarity between an individual’ emotional response to the emotional response of others from the same culture – relates to well-being in a sample of Asian American and European American college students. Using a profile correlation method, we calculated three types of emotional fit based on self-reported emotions, facial expressions, and physiological responses. We then examined the relationships between emotional fit and individual well-being (depression, life satisfaction) as well as collective aspects of well-being, namely collective self-esteem (one’s evaluation of one’s cultural group) and identification with one’s group. The results revealed that self-report emotional fit was associated with greater individual well-being across cultures. In contrast, culture moderated the relationship between self-report emotional fit and collective self-esteem, such that emotional fit predicted greater collective self-esteem in Asian Americans, but not in European Americans. Behavioral emotional fit was unrelated to well-being. There was a marginally significant cultural moderation in the relationship between physiological emotional fit in a strong emotional situation and group identification. Specifically, physiological emotional fit predicted greater group identification in Asian Americans, but not in European Americans. However, this finding disappeared after a Bonferroni correction. The current finding extends previous research by showing that, while emotional fit may be closely related to individual aspects of well-being across cultures, the influence of emotional fit on collective aspects of well-being may be unique to cultures that emphasize interdependence and social harmony, and thus being in alignment with other members of the group

Determinants of Shared and Idiosyncratic Contributions to Judgments of Faces

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[Data for] Individualized Models of Social Judgments and Context-Dependent Representations

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A Consistent but Highly Varied Androcentric Bias in the Visual Representation of “Typical” Faces

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A Consistent but Highly Varied Androcentric Bias in the Visual Representation of “Typical” Faces

Code and dat

Determinants of Shared and Idiosyncratic Contributions to Judgments of Faces

Data and Code for Projec

A Consistent but Highly Varied Androcentric Bias in the Visual Representation of “Typical” Faces

Decades of research have shown that androcentric bias (i.e., assuming that men represent the default) is prevalent across text, images, and social interactions. Despite this evidence, recent research on the mental imagery of faces shows an opposite of the expected androcentric bias: Mental representations of “typical” faces appear more like women than men (Kunst et al., 2023). In the present research, we first aim to reconcile this apparent discrepancy by examining the mental imagery associated with typical persons using generative reverse correlation – a data-driven method that leverages artificial intelligence to construct photo-realistic imagery of visual stereotypes for both groups and individuals. Second, we explore potential reasons for the observed reversal in mental imagery by examining individual differences in typicality judgments. Across two studies, our results show 1) individuals tend to equate “typical persons” with “men” in their mental representations at the group-level; but 2) there is large individual-level variability in both “typical person” mental imagery and explicit “typicality” judgments that complicates conclusions drawn from aggregate-level data