The power of the unexpected: Prediction errors enhance stereotype-based learning

Stereotyping is a ubiquitous feature of social cognition, yet surprisingly little is known about how group-related beliefs influence the acquisition of person knowledge. Accordingly, in combination with computational modeling (i.e., Reinforcement Learning Drift Diffusion Model analysis), here we used a probabilistic selection task to explore the extent to which gender stereotypes impact instrumental learning. Several theoretically interesting effects were observed. First, reflecting the impact of cultural socialization on person construal, an expectancy-based preference for stereotype-consistent (vs. stereotype-inconsistent) responses was observed. Second, underscoring the potency of unexpected information, learning rates were faster for counter-stereotypic compared to stereotypic individuals, both for negative and positive prediction errors. Collectively, these findings are consistent with predictive accounts of social perception and have implications for the conditions under which stereotyping can potentially be reduced.