The study investigates whether large language models (LLMs) can generate mentalization profiles that are structurally coherent with mentalization‑based treatment (MBT) dimensions. Fifty GPT‑4.1 dialogues were produced, and the resulting narrative profiles were rated by five MBT‑trained psychiatrists on evaluative and argumentative coherence across canonical MBT axes and higher‑order dimensions. Ratings showed moderate‑to‑high structural coherence (mean scores 3.63–3.98) with strong interrater reliability (ICC(3,k) 0.60–0.84) and a strong association between evaluative and argumentative coherence (Pearson r 0.826–0.967). A negative control confirmed the specificity of the ratings, and clinicians noted that the outputs were linguistically organized and clinically interpretable, though lacking affective variability and contextual integration.
arXiv:2512. 04124v4 Announce Type: replace-cross Abstract: Frontier language models increasingly participate in conversations about distress and mental health, yet the mechanisms that generate anthropomorphic self narratives remain unclear.
arXiv:2508. 19008v3 Announce Type: replace Abstract: Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis.
arXiv:2604. 19139v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and increasingly conspicuous phenomenon has emerged: the proliferation of verbal tics--repetitive, formulaic linguistic patterns that pervade model outputs.
arXiv:2603.18007v2 Announce Type: replace-cross Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to inf...
arXiv:2608. 07511v1 Announce Type: cross Abstract: Background.
arXiv:2507. 04491v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly being integrated into psychological and behavioral research as research tools, evaluation targets, human simulators, and cognitive models.
The paper introduces the concept of summarization bias in large language models (LLMs), describing a systematic tendency for LLMs to represent narrative meaning as an abstract summary label rather than the reconstructable inferential structure that produces it. It frames this bias within the Bulut Doctrine’s told‑shown axis, arguing that LLMs fail in a specific direction: they default to told‑mode explicitness in generative tasks and reward told‑mode explicitness while under‑detecting shown‑mode suppression in evaluative tasks. The authors outline two regimes of bias, present preliminary evidence, and pre‑register a test protocol to validate or abandon the construct.
arXiv:2609.15522v1 Announce Type: new Abstract: Large language models (LLMs) with human-like performance on linguistic tasks have transformed the study of language in neurodiverse conditions. LLMs pr...
arXiv:2606. 30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric.
arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.
The paper introduces the Schema Coherence Inventory (SCI), a 96-item forced-choice test designed to determine whether a belief assigned to a persona-conditioned language model actually organizes its behavior or merely reflects affect. SCI evaluates belief organization across eight levels of Beck's cognitive model, using length‑normalized log‑likelihood scores and a target‑specificity criterion to distinguish true belief influence from mood effects. Validation across five model backbones (1.7B–14B parameters) and nine clinicians shows high reliability and discriminant validity, and a 48‑item short form correlates strongly with the full inventory.
arXiv:2608.22797v1 Announce Type: new Abstract: Background This study was designed to evaluate whether a domain-specific large language model (LLM) trained exclusively on patient education resources...