LLMorphism: When humans come to see themselves as language models

2026-05-06

AI Summary

This study identifies and examines an emerging cognitive bias—“LLMorphism”—wherein individuals erroneously infer that human cognition operates similarly to large language models (LLMs) due to the latter’s production of human-like language. Integrating philosophical analysis, cognitive science theory, and socio-cultural critique, the research distinguishes LLMorphism from historical notions such as mechanomorphic anthropomorphism and computationalism. It elucidates the psychological and social mechanisms underpinning this bias, particularly analogical transfer and the heuristic availability of metaphors equating minds with LLMs. The paper demonstrates how LLMorphism is increasingly infiltrating domains like education, healthcare, and the workplace, potentially undermining nuanced understandings of human mental uniqueness and warning of attendant risks of cognitive diminishment.

Abstract

LLMorphism is the biased belief that human cognition works like a large language model. I argue that the rise of conversational LLMs may make this bias increasingly psychologically available. When artificial systems produce human-like language, people may draw a reverse inference: if LLMs can speak like humans, perhaps humans think like LLMs. This inference is biased because similarity at the level of linguistic output does not imply similarity in cognitive architecture. Yet, LLMorphism may spread through two mechanisms: analogical transfer, whereby features of LLMs are projected onto humans, and metaphorical availability, whereby LLM vocabulary becomes a culturally salient vocabulary for describing thought. I distinguish LLMorphism from mechanomorphism, anthropomorphism, computationalism, dehumanization, objectification, and predictive-processing theories of mind. I outline its implications for work, education, responsibility, healthcare, communication, creativity, and human dignity, while also discussing boundary conditions and forms of resistance. I conclude that the public debate may be missing half of the problem: the issue is not only whether we are attributing too much mind to machines, but also whether we are beginning to attribute too little mind to humans.

Problem

• LLMorphism
• cognitive bias
• large language models
• human cognition
• anthropomorphism

Innovation

• LLMorphism
• analogical transfer
• metaphorical availability
• reverse inference
• cognitive bias