Detecting social biases using mental state inference.

Social biases are prevalent in everyday social interactions, but they are often expressed in subtle ways that can make them difficult to detect. Yet, intuitively, people often recognize when they are the subject of a bias, even when those biases are not explicitly communicated (e.g., sexist or racist slurs). While much research has focused on the negative consequences of social biases, less is known about the cognitive mechanisms that allow people to explicitly detect them in the first place. In this article, we propose an account of social bias detection grounded in mental state representations. We propose that people infer biases by detecting a gap between expected unbiased behavior and observed behavior, which in turn reveals the underlying biases influencing other people's beliefs. We present a formal computational model of this account and, across four preregistered experiments (n = 876 total), show that this model captures participants' inferences about an agent's prior beliefs (Experiment 1), general social biases (Experiment 2) across various real-world contexts (Experiments 3a-3c), and even specific racial and gender biases (Experiment 4). We compare this model with alternative models that differ in their assumptions about whether and how a biased agent updates their beliefs about an individual. Participants' judgments were best explained as the process of inferring an agent's prior beliefs before updating them based on available evidence about the individual. These findings highlight the role of mental state reasoning in bias detection and broaden our understanding of the human capacity to detect and reason about social biases. (PsycInfo Database Record (c) 2025 APA, all rights reserved).