The events surrounding the 2020 U.S. election and the January 6 insurrection have challenged scholarly understanding of concepts like collective action, radicalization, and mobilization. In this article, we argue that online far-right radicalization is better understood as a form of distributed cognition, in which the groups’ online environment incentivizes certain patterns of behavior over others. Namely, these platforms organize their users in ways that facilitate a nefarious form of collective intelligence, which is amplified and strengthened by systems of algorithmic curation. In short, these platforms reflect and facilitate undemocratic cognition, fueled by affective networks, contributing to events like the January 6 insurrection and far-right extremism more broadly. To demonstrate, we apply this framing to a case study (the “Stop the Steal” movement) to illustrate how this framework can make sense of radicalization and mobilization influenced by undemocratic cognition.

围绕 2020 年美国大选和 1 月 6 日起义的事件挑战了学术界对集体行动、激进化和动员等概念的理解。在本文中，我们认为网络极右翼激进化被更好地理解为一种分布式认知形式，其中群体的在线环境会激励某些行为模式而不是其他行为模式。也就是说，这些平台以促进邪恶形式的集体智慧的方式组织他们的用户，这种集体智慧被算法管理系统放大和加强。简而言之，这些平台反映并促进了不民主的认知，在情感网络的推动下，促成了 1 月 6 日起义和更广泛的极右翼极端主义等事件。为了证明这一点，我们将这一框架应用于一个案例研究（“停止偷窃”运动），以说明该框架如何理解受不民主认知影响的激进化和动员。