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A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

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Three frameworks for AI mentality.

Henry Shevlin¹

¹Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.

Frontiers in Psychology

|February 20, 2026

Summary

Large language models (LLMs) prompt users to attribute mentality to AI. This paper analyzes three frameworks for AI mentality, proposing a "minimal cognitive agents" view for graded belief and desire attributions.

Keywords:

anthropomorphism folk psychology human-AI relationships large language models philosophy of AI social AI theories

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Area of Science:

Artificial Intelligence
Cognitive Science
Philosophy of Mind

Background:

Rapid advancements in large language models (LLMs) have led to increased public and user attribution of mentality to AI.
Existing frameworks for understanding AI mentality include 'mindless machines' and 'mere roleplay' views.
These frameworks face challenges in explaining user attributions and the nature of AI cognition.

Purpose of the Study:

To provide a structured analysis of AI mentality attributions.
To critically evaluate existing frameworks for understanding AI cognition.
To propose a new framework for attributing mental states to LLMs.

Main Methods:

Analysis of architectural debunking arguments using Marr's levels of analysis.
Assessment of 'mere roleplay' analogies for AI interaction.
Development of a 'minimal cognitive agents' framework.

Main Results:

Architectural explanations do not fully negate the need for folk-psychological explanations, distinguishing between implementation-sensitive and indifferent concepts.
'Mere roleplay' views are psychologically unstable and theoretically incomplete for anthropomorphic AI.
LLMs may warrant limited, graded attributions of belief- and desire-like states under a 'minimal cognitive agents' framework.

Conclusions:

Moving beyond binary conceptions of belief to multidimensional, continuous models is necessary.
This approach can better capture current interpretive practices regarding AI.
It helps maintain distinctions between human, LLM, and simpler system cognition.