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Open Access Publications from the University of California

STEREONET: A Network Approach for Stereotype Change

Creative Commons 'BY' version 4.0 license
Abstract

Stereotypes change over time and across cultures, but they are hard to change in experiments. This paper proposes a solution by reconceptualizing stereotypes not as simple associations between groups and traits, but as rich networks of interconnected concepts spanning multiple domains. Building on cross-domain mapping, we used a simple question-answering task (''If X were a Y, what Y would it be?'') to create the expansive networks of 100 social groups to eight domains including animals, jobs, sports, colors, beverages, vehicles, musical instruments, and academic subjects. We found, for instance, the stereotype of women lacking agency is part of a larger network where women are associated with preferences for certain drinks such as wine, colors of pink, musical instruments of harps, or sports such as softball. We further tested whether rewiring these broader networks could change stereotypes more effectively than prior methods. Network-based interventions showed promising results for some groups (women were seen as more competent and Muslims were viewed as more friendly), but effects varied for different groups (minimal changes for criminals). This work suggests that successful stereotype change may require engaging with broader networks of subtle, seemingly unrelated associations rather than targeting individual stereotypical beliefs in isolation.