Individualized Models of Social Judgments and Context-Dependent Representations

Abstract

How individuals view the world is critical to understanding human behavior. Yet, almost all research within perception 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. a trustworthy individual to fix your car). Across all studies, we highlight the theoretical implications and utility of developing idiosyncratic models of visual perception.

Determinants of Shared and Idiosyncratic Contributions to Judgments of Faces

“…That is, responses and analyses are aggregated across all individuals sampled and inferences are drawn about judgments as a group level behavior. However, a growing body of literature suggests that a substantial portion of the variance of judgments is not explained by models that aggregate judgments (Albohn et al, , 2024Peterson et al, 2022;Todorov & Oh, 2021;Zhan et al, 2021). While this unexplained variance is traditionally treated as "noise", a sizable portion is also meaningfully related to the idiosyncrasies of the participants making judgments; oftentimes over half of the meaningful variance is explained by participant idiosyncrasies.…”

“…This would be an interesting avenue for future work to explore, as current research has yet to tease apart and compare the predictive power of different averaged and idiosyncratic variables within the interaction component itself, as the participant-by-stimulus interaction is likely made up of some weighted combination of both "group averaged idiosyncrasies" as well as irreducible idiosyncrasies. One recent approach has utilized generative modeling to visualize photorealistic, individualized models of judgments of faces (Albohn et al, , 2024. As technology advances, the resolution and precision of individualized models will increase in predictive power allowing for a more nuanced understanding of important aggregate (at any level of aggregation) and irreducible idiosyncratic predictors.…”