Year: 2026
Type: article
Abstract: Rapid advances in large language models (LLMs) have been accompanied by a striking increase in public and user attribution of mentality to AI systems. This paper offers a structured analysis of these attributions by distinguishing three frameworks for thinking about AI mentality and their implications for interpretation. First, I examine "mindless machines" views, focusing on architectural debunking arguments that claim mechanistic or algorithmic descriptions render folk-psychological explanation redundant. Drawing on Marr's levels of analysis, I argue that such arguments are often too quick, though they highlight an important distinction between "deep" folk-psychological concepts that are sensitive to implementation and "shallow" concepts such as belief and desire that are more architecture-indifferent. Second, I assess "mere roleplay" views that treat mental-state ascriptions to LLMs as useful heuristics akin to engagement with fiction. I argue that this stance is psychologically unstable in anthropomimetic systems designed to elicit unironic anthropomorphism, and theoretically incomplete insofar as roleplay analogies typically presuppose an underlying agent. Third, I develop a "minimal cognitive agents" framework under which LLMs may warrant limited, graded attributions of belief- and desire-like states. I suggest that moving from binary to multidimensional, continuous conceptions of belief can preserve distinctions between humans, LLMs, and simpler systems while better capturing emerging interpretive practice and its normative stakes.
Source: Frontiers in Psychology
Author: Henry Shevlin
Institution: University of Cambridge
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: Leverhulme Trust
Award: G1515709144
OpenAlex ID: W7127625689