Samuel A. W. Klein

Racially biased weapon identification, wherein guns are identified more easily after seeing Black (vs. White) face primes, is a robust and replicable phenomenon. Mounting evidence suggests that introducing additional facial information (e.g., varying age cues) does not meaningfully alter this racial bias. Only when augmenting its relative salience does the additional, nonrace information appear to mitigate racially biased weapon identification. Even when reducing racial bias by enhancing nonrace facial cues, social information is typically communicated via the face, a context in which race may be particularly salient. Two experiments (N total = 590 participants) using a sequential priming task tested whether broadening the contextual information in primes to include both faces and bodies moderates racially biased weapon identification (gun vs. tool) decisions. Replicating past findings, racial bias was evident when primes cued age and race via facial information only. However, this behavioral effect disappeared when primes included both faces and bodies, providing richer social context. Diffusion decision modeling revealed that race cues shifted the starting point of the decision-making process toward stereotype-consistent responses (e.g., "gun" after Black primes) with face-only primes, but this processing bias disappeared with face-and-body primes. Multinomial processing tree modeling further revealed attenuated attention to race in face-and-body (vs. face-only) primes, whereas attention to age remained intact across conditions. These findings advance theory on the operation of racially biased decision making in richer social contexts.

Forming impressions of others is a fundamental aspect of social life. These impressions necessitate the integration of many and varied sources of information about other people, including social group memberships, apparent personality traits, inferences from observed behaviors, and so forth. However, methodological limitations have hampered progress in understanding this integration process. In particular, extant approaches have been unable to measure the independent contributions of multiple features to a given impression. In this article, after describing these limitations and their constraints on theory testing and development, we present a multinomial processing tree model as a computational solution to the problem. Specifically, the model distinguishes the contributions of multiple cues to social judgment. We describe an empirical demonstration of how applying the model can resolve long-standing debates among person perception researchers. Finally, we survey a variety of questions to which this approach can be profitably applied.

Extending evidence for the rapid revision of mental representations of what other people are like, we explored whether people also rapidly revise their representations of what others look like. After learning to ascribe positive or negative behavioral information to a target person and generating a visualization of their face in a reverse-correlation task, participants learned new information that was (a) counter-attitudinal and diagnostic about the person's character or (b) neutral and non-diagnostic, and then they generated a second visualization. Ratings of these visualizations in separate samples of participants consistently revealed revision effects: Time 2 visualizations assimilated to the counter-attitudinal information. Weaker revision effects also emerged after learning neutral information, suggesting that the evaluative extremity of visualizations may dilute when encountering any additional information. These findings indicate that representations of others' appearance may change upon learning more about them, particularly when this new information is counter-attitudinal and diagnostic.