How does LLM vocabulary spread beliefs about human thinking?
When LLM concepts become the everyday language for describing thought, do people unconsciously adopt LLM-like models of cognition? This explores how metaphor and lexical availability might reshape self-understanding without explicit argument.
If LLMorphism is the biased belief that human cognition works like a large language model, the more useful question is how such a belief propagates. The argument identifies two mutually reinforcing mechanisms. The first is analogical transfer: features salient in LLMs — predicting the next word, hallucinating, being "prompted," running on training data — get projected onto humans, so we start describing memory as retrieval, creativity as recombination, and bias as a training artifact. The second is metaphorical availability: as conversational LLMs saturate daily life, their vocabulary becomes the most psychologically available language for talking about thought at all, the way "wired" and "hardwired" became default once computers were ubiquitous.
The pattern is worth isolating because it explains why the bias can spread without anyone explicitly endorsing the claim. Nobody needs to argue that humans are language models; the lexicon does the work by making the comparison feel natural every time someone reaches for it. This connects LLMorphism to a longer history of cognitive metaphors borrowed from dominant technologies — the brain as clockwork, as telephone switchboard, as computer — each of which reshaped self-understanding more than it described it. The counterpoint is that metaphors can illuminate as well as distort; the risk is specifically that LLM metaphors flatter their own architecture, smuggling in the premise that prediction exhausts cognition. Resistance therefore has to operate at the level of language, not just argument.
Inquiring lines that use this note as a source 4
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- Do LLMs genuinely internalize human psychological structure or match surface patterns?
- How do LLMs access and draw on the same shared symbolic universe as humans?
- Why do users attribute beliefs to LLMs despite uncertainty about their minds?
- Why do cognitive metaphors change based on available technology?
Related concepts in this collection 4
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Does human language use ever exist outside communication?
Explores whether humans can use language in non-communicative ways, or whether the communicative scaffold learned in childhood persists through all language use including private writing and internal thought.
the embodied-communicative basis of human language is exactly what the metaphor erases
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Are we underestimating human minds while debating machine minds?
Public AI discourse focuses on whether machines have too much attributed mind, but what if the real risk is humans coming to see themselves as mere language models? This explores the neglected inverse problem.
names the asymmetric harm of this propagation: LLMorphism deflates the human side of the comparison while the debate fixates on inflating the machine side
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How does AI-assisted work reshape how people see their own abilities?
When users delegate tasks to AI, do they unknowingly integrate the system's outputs into their sense of personal competence? This explores whether AI interaction produces a specific form of self-perception distortion distinct from trust or effort issues.
gives the propagated belief a sharp construct: LLMorphism's two mechanisms are how the self-perception-level LLM Fallacy spreads through a culture
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Are language models developing real functional competence or just formal competence?
Neuroscience suggests formal linguistic competence (rules and patterns) and functional competence (real-world understanding) rely on different brain mechanisms. Can next-token prediction alone produce both, or does it leave functional competence behind?
grounds the distortion the metaphor smuggles in: equating prediction with cognition collapses a distinction the neuroscience keeps separate
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- LLMorphism: When humans come to see themselves as language models
- From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
- Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
- Computational structuralism: Toward a formal theory of meaning in the age of digital intelligence
- Large Language Models Do Not Simulate Human Psychology
- Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
- Word Meanings in Transformer Language Models
- How new data permeates LLM knowledge and how to dilute it
Original note title
llmorphism spreads through analogical transfer and metaphorical availability as llm vocabulary becomes the cultural lexicon for thought