cs.CLNov 20, 2025

Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in Mentalization

Authors: Stefano Epifani, Giuliano Castigliego, Giuliano Razzicchia, Elisabeth Seiwald-Sonderegger, Laura Kecskeméti

Organizations: Digital Transformation Institute, Rome, Italy · Department of Communication and Social Research, Sapienza University of Rome, Italy · Psychoanalytic Academy of Italian-Speaking Switzerland, Chur, Switzerland · Psychiatric Services of the Canton of Grisons, Chur, Switzerland

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Mentalization is a core human capacity involving the interpretation of one's own and others' behavior in terms of underlying mental states. Large language models (LLMs) can generate linguistically reflective discourse, raising the question of whether the formal linguistic structure of mentalization can be reproduced without experiential and affective processes. This study assessed whether an LLM can generate mentalization profiles structurally coherent with the dimensions of mentalization-based treatment (MBT) and recognized as formally mentalizing by expert clinicians. Fifty human-model dialogues were generated with GPT-4.1 under standardized conditions; at the end of each, the model produced a narrative mentalization profile. Five MBT-trained psychiatrists independently rated each profile on 4 canonical MBT axes and 2 higher-order dimensions (regulatory functions, synthetic profile) for evaluative and argumentative coherence. Interrater reliability was assessed with ICC(3,k). Mean scores ranged from 3.63 to 3.98, indicating moderate-to-high structural coherence; ICC(3,k) in the evaluative domain ranged from 0.60 to 0.84. Evaluative and argumentative coherence were strongly associated (Pearson r 0.826-0.967). A negative control with mismatched dialogue-profile pairs yielded minimal ratings. Clinicians described outputs as linguistically organized and clinically interpretable, but with limited affective variability and contextual integration. The findings suggest that LLMs can reproduce the formal linguistic structure of reflective discourse - a phenomenon we term algorithmic reflectivity - which appears more consistent with structural language regularities than with experiential or intentional processes. LLMs may serve as research and training tools for studying the linguistic aspects of mentalization.

More in this topic

Self-Reflection in Language Model Agents

See all 39 papers

Explore similar work

CardsList
Aug 26, 2026

Assessing mentalization in humans and large language models

Mentalization - the ability to infer others' beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social interactions. Large language models (LLMs) demonstrate behaviour consistent with humans on theory-of-mind tasks, yet whether these models can guide adaptive behaviour through mentalization is unknown. Here we use two economic games with cognitive computational modeling to uncover the latent strategies underlying mentalization in LLMs. We tested individual LLM agents across four model families, DeepSeek, GPT-4.1, GPT-5 and Gemini 2.0 Flash (N = 2,099), against opponents of varying sophistication and examined whether a prompting strategy designed to elicit strategic reasoning improved performance. We benchmarked results against human participants (N = 251) as a comparative measure. Across both games, LLMs showed clear behavioural and computational signatures of mentalizing that differed markedly by model provider and size. Strategic prompting generally improved performance by inducing more sophisticated reasoning, yet the extent of the benefit differed across the two tasks. Last, GPT-5 agents flexibly adapted their recursive depth of reasoning to increasingly sophisticated opponents, demonstrating superior performance to human participants. Collectively, we demonstrate different capacities for mentalization across LLMs, and highlight cognitive computational modeling as a formal method for assessing comparative intelligence across humans and machines.

Aamir Sohail, Xintong Zhong, Arkady Konovalov +2

Centre for Human Brain Health, University of Birmingham, Birmingham, UK. · School of Psychology, University of Birmingham, Birmingham, UK. · Institute for Mental Health, University of Birmingham, Birmingham, UK. +3

Social ReasoningTheory of Mind
AskPDF
Sep 2, 2025

LLMs and their Limited Theory of Mind: Evaluating Mental State Annotations in Situated Dialogue

What if large language models could not only infer human mindsets but also expose every blind spot in team dialogue such as discrepancies in the team members' joint understanding? We present a novel, two-step framework that leverages large language models (LLMs) both as human-style annotators of team dialogues to track the team's shared mental models (SMMs) and as automated discrepancy detectors among individuals' mental states. In the first step, an LLM generates annotations by identifying SMM elements within task-oriented dialogues from the Cooperative Remote Search Task (CReST) corpus. Then, a secondary LLM compares these LLM-derived annotations and human annotations against gold-standard labels to detect and characterize divergences. We define an SMM coherence evaluation framework for this use case and apply it to six CReST dialogues, ultimately producing: (1) a dataset of human and LLM annotations; (2) a reproducible evaluation framework for SMM coherence; and (3) an empirical assessment of LLM-based discrepancy detection. Our results reveal that, although LLMs exhibit apparent coherence on straightforward natural-language annotation tasks, they systematically err in scenarios requiring spatial reasoning or disambiguation of prosodic cues.

Katharine Kowalyshyn, Matthias Scheutz

Department of Computer Science Tufts University

LLM EvaluationTheory of MindLLM-Assisted Annotation
AskPDF
Sep 19, 2026

Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling

Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize systematically. We develop a cross-linguistic psychometric profiling framework and evaluate nine LLMs using seven psychological instruments, with five repeated administrations per model and language in Chinese and English. Items unresolved after a prespecified retry procedure are retained as NA. Joint analysis of scored and NA responses captures response tendencies and boundaries of self-report applicability. LLMs exhibit structured, model-specific profiles despite a shared alignment-shaped pattern of higher prosocial and self-regulatory responses and lower dominance, disengagement and harmful-intent endorsement. NA responses are structured rather than uniformly distributed, indicating where outputs are treated as inapplicable, refused or cannot be mapped to valid response options. Language condition and provider origin are associated with profile configuration and answerability, whereas repeated administrations show high reproducibility and permit recovery of model identity. Human-reference and prompt-robustness analyses further indicate that these signatures are context dependent. Joint analysis of psychometric profiling and answerability offers a framework for quantifying deployment-level behavioural signatures.

Yu Sha, Junqi Tao, Dixin Zhou +10

School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China. · Key Laboratory of Quark & Lepton Physics and Institute of Particle Physics, Central China Normal University, Wuhan, 430079, China. · Shandong Science and Technology Press, Shandong Publishing Group, Jinan, 250003, China. +6

Personality Modeling in Language ModelsMultilingual Language Model EvaluationLanguage Model FingerprintingCross-Lingual Consistency in Language Models
AskPDF