The Anthropomorphic Trap: A Systematic Analysis of Category Errors in LLM Research

In spirit photographs, people perceive faces of the deceased in random photographic noise —a perceptual phenomenon known as pareidolia. This paper argues that a structurallyidentical mechanism operates in AI research: researchers perceive minds, personalities, andintentions in the statistical outputs of large language models (LLMs). What Floridi (2025)terms "semantic pareidolia" arises from the brain's predictive processing applying its mostaccessible generative model — "intentional agent" — to fluent linguistic stimuli. Withanthropomorphic terminology now appearing in 40% of LLM research papers (Ibrahim &Cheng, 2025), we provide the first systematic analysis of these attributions as a unifiedphenomenon. We propose a taxonomy of five types of anthropomorphic category error andarticulate three cross-cutting principles of invalidity — the Valid Range Principle, theOntological Stability Principle, and the Purpose Integrity Principle — demonstrating thatanthropomorphic attributions fail because the instruments are miscalibrated, the subjectlacks ontological stability, and the purpose of measurement is distorted. We trace thestructural causes of persistence and identify consequences including safety researchdistortion, policy misframing, and concrete harms to vulnerable human populations throughNormative Drift and clinical tool authority erosion. Finally, we survey mathematicallygrounded alternative frameworks — dynamical systems theory, information-theoreticcompression, topological data analysis, and reliability engineering — that describe LLMbehavior with greater precision and no dependence on psychological vocabulary. The mindwe see in the model remains a statistical pattern.