4.1 Article

Stereotypes and self-fulfilling prophecies in the Bayesian brain

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Publisher

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/0020174X.2023.2166983

Keywords

Stereotypes; self-fulfilling prophecy; predictive processing; perceptual inference; active inference

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A minority of social psychologists argue that stereotype accuracy is a robust finding, while also questioning the power of self-fulfilling prophecies. This paper examines the debate from the perspective of predictive processing, suggesting that stereotypes serve a purpose in making the social world predictable and that self-fulfilling prophecies greatly impact social reality.
Stereotypes are often described as being generally inaccurate and irrational. However, for years, a minority of social psychologists has been proclaiming that stereotype accuracy is among the most robust findings in the field. This same minority also opposes the majority by questioning the power of self-fulfilling prophecies and thereby the construction of social reality. The present paper examines this long-standing debate from the perspective of predictive processing, an increasingly influential cognitive science theory. In this theory, stereotype accuracy and self-fulfilling prophecies are two sides of the same coin, namely prediction error minimisation, pointing to a new middle course between the two existing views. On the one hand, predictive processing indicates that depicting stereotypes as generally inaccurate runs counter to their actual purpose of making the social world predictable, which supports the minority view. On the other hand, predictive processing supposes that expectations, including stereotypes, permanently affect perception and behaviour and thereby co-construct social reality, which supports the majority view. Therefore, from a predictive processing perspective, stereotypes are largely rational and not per se inaccurate, and self-fulfilling prophecies are omnipresent and greatly affect social reality. This new middle course appears to fit the empirical data better than the two existing views.

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