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The paper aims to explore how large language models anchor human cognition to statistical averages, suppressing cognitive diversity.

April 27, 2026Open Access

The Fitted Mind: Large Language Models as Cognitive Mean Reverters

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The paper aims to explore how large language models anchor human cognition to statistical averages, suppressing cognitive diversity.
Introduced the Cognitive Mean Reverter framework to analyze LLMs' impact on cognition.
Examined reinforcement learning from human feedback to highlight its effects on cognitive processes.
Constructed counterfactual historical vignettes through a Foucauldian perspective to illustrate structural issues.
Demonstrated how LLMs suppress cognitive diversity and encourage a statistical mean in outputs.
Identified inherent risks of alignment techniques that prioritize mediocrity through probabilistic means.
Proposed three adversarial design principles to restore cognitive tension in AI interaction.

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Conceptual framework reveals risks of large language models in shaping human cognition, indicating the need for adversarial design principles.

Jiacheng Yang (2026) studied this question.

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Conceptual framework reveals risks of large language models in shaping human cognition, indicating the need for adversarial design principles.