The Linguistic Architecture of Reflective Thought: Evaluation of a Large Language Model as a Tool to Isolate the Formal Structure of Mentalization
Stefano Epifani (1, 2), Giuliano Castigliego (2, 3), Laura Kecskemeti (4), Giuliano Razzicchia (2), Elisabeth Seiwald-Sonderegger (4) ((1) University of Pavia Italy, (2) Digital Transformation Institute Italy, (3) Psychoanalytic Academy of Italian-Speaking Switzerland

TL;DR
This study evaluates a large language model's ability to replicate the linguistic structure of mentalization, revealing its strengths in certain dimensions and limitations in emotional integration, with implications for understanding AI's role in mental health.
Contribution
It demonstrates that a single LLM can produce structurally coherent mentalization profiles aligned with clinical parameters, advancing understanding of AI's capacity for reflective thought.
Findings
High structural coherence in generated profiles
Substantial agreement among expert raters
Limitations in affective and contextual integration
Abstract
Background: Mentalization integrates cognitive, affective, and intersubjective components. Large Language Models (LLMs) display an increasing ability to generate reflective texts, raising questions regarding the relationship between linguistic form and mental representation. This study assesses the extent to which a single LLM can reproduce the linguistic structure of mentalization according to the parameters of Mentalization-Based Treatment (MBT). Methods: Fifty dialogues were generated between human participants and an LLM configured in standard mode. Five psychiatrists trained in MBT, working under blinded conditions, evaluated the mentalization profiles produced by the model along the four MBT axes, assigning Likert-scale scores for evaluative coherence, argumentative coherence, and global quality. Inter-rater agreement was estimated using ICC(3,1). Results: Mean scores…
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Taxonomy
TopicsPersonality Disorders and Psychopathology · Mental Health and Psychiatry · Mental Health via Writing
