Ideology and Predictive Processing: Coordination, Bias, and Polarization in Socially Constrained Error Minimization

Authors

Nathan E. Wheeler, University of Toronto
Suraiya Allidina, University of Toronto
Elizabeth U. Long, University of Toronto
Stephen P. Schneider, University of Illinois at Urbana-Champaign
Ingrid J. Haas, University of Nebraska-LincolnFollow
William A. Cunningham, University of Toronto

ORCID IDs

Haas 0000-0002-7316-6605

Document Type

Article

Date of this Version

2020

Citation

Current Opinion in Behavioral Sciences (2020) 34: 192-198

doi: 10.1016/j.cobeha.2020.05.002

Comments

Copyright 2020, Elsevier. Used by permssion

Abstract

Recent models of cognition suggest that the brain may implement predictive processing, in which top-down expectations constrain incoming sensory data. In this perspective, expectations are updated (error minimization) only if sensory data sufficiently deviate from these expectations (prediction error). Although originally applied to perception, predictive processing is thought to generally characterize cognitive architecture, including the social cognitive processes involved in ideological thinking. Scaling up these simple computational principles to the social sphere outlines a path by which group members may adopt shared ideologies and beliefs to predict behavior and cooperate with each other. Because ideological judgments are of specific interest to others in our political groups, we may increasingly regulate each other’s thinking, sharing the process of error minimization. In this paper, we outline how this process of shared error minimization may lead to shared ideologies and beliefs that allow group members to predict and cooperate with each other, and how, as a consequence, political polarization and extremism may result.