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Abstract Cesario claims that all bias research tells us is that people “end up using the information they have come to learn as being probabilistically accurate in their daily lives” (sect. 5, para. 4). We expose Cesario's flawed assumptions about the relationship between accuracy and bias. Through statistical simulations and empirical work, we show that even probabilistically accurate responses are regularly accompanied by bias. 

When seeing a face, people form judgments of perceptually ambiguous social categories (PASCs), for example, gun-owners, gay people, or alcoholics. Previous research has assumed that PASC judgments arise from the statistical learning of facial features in social encounters. We propose, instead, that perceivers associate facial features with traits (e.g., extroverted) and then infer PASC membership via learned stereotype associations with those traits. Across three studies, we show that when any PASC is more stereotypically associated with a trait (e.g., alcoholics = extroverted), perceivers are more likely to infer PASC membership from faces conveying that trait (Study 1). Furthermore, we demonstrate that individual differences in trait–PASC stereotypes predict face-based judgments of PASC membership (Study 2) and have a causal role in these judgments (Study 3). Together, our findings imply that people can form any number of PASC judgments from facial appearance alone by drawing on their learned social–conceptual associations. 

People automatically infer others’ personality (e.g., trustworthiness) based on facial appearance, and such facial stereotype biases predict real-world consequences across political, legal, and business domains. The present research tested whether these biases can be mitigated through counterstereotype training aimed at reconfiguring the associations between specific facial appearances and social traits. Across six studies and a replication, a behavioral counterstereotype training consistently reduced or eliminated facial stereotype biases for White male faces in the context of economic trust games, hiring decisions, and even automatic evaluations assessed via evaluative priming. Together, the results demonstrate a fundamental malleability in facial stereotyping related to trustworthiness, with a minimal training able to mitigate the tendency to activate and apply long-held, highly automatized facial stereotypes. These findings suggest that face impressions are more flexible than typically appreciated, and they provide a potential inroad toward combating our ingrained biases based on facial appearance. 

Abstract Across multiple domains of social perception - including social categorization, emotion perception, impression formation, and mentalizing - multivariate pattern analysis (MVPA) of fMRI data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.