LLMorphism: When humans come to see themselves as language models

TL;DR

The paper discusses LLMorphism, a bias where humans see themselves as similar to language models, and explores its psychological implications and potential societal impacts.

Contribution

It introduces the concept of LLMorphism, distinguishing it from related biases, and analyzes how it may influence perceptions of human cognition and societal values.

Findings

LLMorphism may spread through analogical transfer and metaphorical language.

It has implications for work, education, healthcare, and human dignity.

The bias could lead to underestimating human cognitive complexity.

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.