Stereotypes disrupt probabilistic category learning.

Racial stereotypes exert pernicious effects on decision-making and behavior, yet little is known about how stereotypes disrupt people's ability to learn new associations. The current research interrogates a fundamental question about the boundary conditions of probabilistic learning by examining whether and how learning is influenced by preexisting associations. Across three experiments, participants learned the probabilistic outcomes of different card combinations based on feedback in either a social (e.g., forecasting crime) or nonsocial (e.g., forecasting weather) learning context. During learning, participants were presented with either task-irrelevant social (i.e., Black or White faces) or nonsocial (i.e., darker or lighter clouds) stimuli that were stereotypically congruent or incongruent with the learning context. Participants exhibited learning disruptions in the social compared to nonsocial learning context, despite repeated instructions that the stimuli were unrelated to the outcome (Studies 1 and 2). We also found no differences in learning disruptions when participants learned in the presence of negatively (Black and criminal) or positively valenced stereotypes (Black and athletic; Study 3). Finally, we tested whether learning decrements were due to "first-order" stereotype application or inhibition at the trial level, or due to "second-order" cognitive load disruptions that accumulate across trials due to fears of appearing prejudiced (aggregated analysis). We found no evidence of first-order disruptions and instead found evidence for second-order disruptions: participants who were more internally motivated to respond without prejudice, and thus more likely to self-monitor their responses, learned less accurately over time. We discuss the implications of the influence of stereotypes on learning and memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Computational underpinnings of partisan information processing biases and associations with depth of cognitive reasoning.

Despite unprecedented access to information, partisans increasingly disagree about basic facts that are backed by data, posing a serious threat to a democracy that relies on finding common ground based on objective truths. We examine the underpinnings of this phenomenon using drift diffusion modeling (DDM). Partisans (N = 148) completed a sequential sampling task where they evaluated the honesty of Democrat or Republican politicians during a debate based on fact-check scores. We found that partisans required less and weaker evidence to correctly categorize the ingroup as more honest, and were more accurate on trials when the ingroup candidate was more honest, compared to the outgroup. DDM revealed that such tendencies arise from both a prior preference for categorizing the ingroup as more honest (i.e., biased starting point) and more precise accumulation of information favoring the ingroup candidate compared to the outgroup (i.e., biased drift rate). Moreover, individual differences in cognitive reasoning moderated task performance for the most devoted partisans and maintained divergent associations with the DDM parameters. This suggests that partisans may reach biased conclusions via different pathways depending on their depth of cognitive reasoning. These findings provide key insights into the mechanisms driving partisan divides in polarized environments, and can inform interventions that reduce impasse and conflict.

Partisan-motivated sampling: Re-examining politically motivated reasoning across the information processing stream.

The United States is increasingly politically polarized, fueling intergroup conflict and intensifying partisan biases in cognition and behavior. To date, research on intergroup bias has separately examined biases in how people search for information and how they interpret information. Here, we integrate these two perspectives to elucidate how partisan biases manifest across the information processing stream, beginning with (a) a biased selection of information, leading to (b) skewed samples of information that interact with (c) motivated interpretations to produce evaluative biases. Across three experiments and four internal meta-analyses, participants (N = 2,431) freely sampled information about ingroup and outgroup members or ingroup and outgroup political candidates until they felt confident to evaluate them. Across experiments, we reliably find that most participants begin sampling information from the ingroup, which was associated with individual differences in group-based motives, and that participants sampled overall more information from the ingroup. This sampling behavior, in turn, generates more variability in ingroup (relative to outgroup) experiences. We find that more variability in ingroup experiences predicted when participants decided to stop sampling and was associated with more biased evaluations. We further demonstrate that participants employ different sampling strategies over time when the ingroup is de facto worse-obfuscating Real Group Differences-and that participants selectively integrate their experiences into evaluations based on congeniality. The proposed framework extends classic findings in psychology by demonstrating how biases in sampling behavior interact with motivated interpretations to produce downstream evaluative biases and has implications for intergroup bias interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).