Year: 2024
Type: article
Abstract: Counterstereotypes challenge the deleterious effects that gender-typed beliefs exert on people's occupational aspirations and lifestyle choices. Surprisingly, however, the critical issue of how readily unexpected person-related knowledge can be acquired remains poorly understood. Accordingly, in two experiments in which the facial appearance of targets was varied to manipulate goodness-of-stereotype-fit (i.e., high vs. low femininity/masculinity), here we used a probabilistic selection task to probe the rate at which counter-stereotypic and stereotypic individuals can be learned. Whether occupational (Expt. 1) or trait-related (Expt. 2) gender stereotypes were explored, a computational analysis yielded consistent results. Underscoring the potency of surprising information (i.e., facial misfits), knowledge acquisition was accelerated for unexpected compared to expected persons, both in counter-stereotypic and stereotypic learning contexts. These findings affirm predictive accounts of social perception and speak to the optimal characteristics of interventions designed to reduce stereotyping outside the laboratory.
Source: Scientific Reports
Authors: Marius Golubickis, Linn M. Persson, Johanna K. Falbén, Siew Hwee Seow, Parnian Jalalian, Yadvi Sharma, Margarita Ivanova, C. Neil Macrae
Institutions: University of Aberdeen, University of Amsterdam
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,590
OpenAlex ID: W4401728311