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

Johanna Katariina Falben* (Corresponding Author), Marius Golubickis, Dimitra Tsamadi, Linn Maria Persson, Colin Macrae

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number105386
Number of pages10
Early online date9 Feb 2023
Publication statusPublished - Jun 2023

Bibliographical note

Johanna Falbén was supported by a European Research Council consolidator grant (817492-SAMPLING).

Data Availability Statement

Code availability
Available on OSF

Data availability
Available on OSF


  • stereotyping
  • person perception
  • reinforcement learning
  • prediction errors
  • drift diffusion model


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