大语言模型作为神经多样性成人心理测量剖面的模拟代理
Large Language Models as Simulative Agents for Neurodivergent Adult Psychometric Profiles
AI总结：
本研究探讨大语言模型能否基于结构化访谈生成接近真实个体的心理测量反应，发现其在神经发育特征模拟中表现优异，但存在特定局限性。
摘要：
成人神经多样性，包括注意缺陷/多动障碍（ADHD）、高功能自闭症谱系障碍（ASD）和认知脱节综合征（CDS），以显著的症状重叠为特征，限制了标准心理测量工具的区分敏感性。尽管最近的研究表明，大语言模型（LLMs）可以从定性数据中模拟人类心理测量反应，但尚不清楚它们是否能准确且稳定地建模神经发育特征，而不仅仅是广泛的人格特征。本研究检验了当LLMs基于结构化的定性访谈进行推理时，是否能生成接近真实个体的心理测量反应，以及这些模拟是否对特质强度的变化敏感。26名成年人完成了29项开放性访谈和四项标准化自我报告量表（ASRS、BAARS-IV、AQ、RAADS-R）。两个LLMs（GPT-4o和Qwen3-235B-A22B）被提示从访谈内容推断个体心理剖面，并对每个量表进行角色扮演回应。通过群体水平比较、误差度量、精确匹配评分和随机基线评估准确性、可靠性和敏感性。两个模型在所有量表上均优于随机响应，其中GPT-4o在准确性和可重复性上表现更高。模拟响应在ASRS、BAARS-IV和RAADS-R上与人类数据紧密匹配，而AQ揭示了子量表特定的局限性，特别是在细节关注度方面。总体而言，研究结果表明，基于访谈的LLMs能够生成连贯且高于偶然率的神经发育特征模拟，支持其在早期心理测量研究中作为合成参与者的潜力，同时突显了明确的领域特定限制。
英文摘要：
Adult neurodivergence, including Attention-Deficit/Hyperactivity Disorder (ADHD), high-functioning Autism Spectrum Disorder (ASD), and Cognitive Disengagement Syndrome (CDS), is marked by substantial symptom overlap that limits the discriminant sensitivity of standard psychometric instruments. While recent work suggests that Large Language Models (LLMs) can simulate human psychometric responses from qualitative data, it remains unclear whether they can accurately and stably model neurodevelopmental traits rather than broad personality characteristics. This study examines whether LLMs can generate psychometric responses that approximate those of real individuals when grounded in a structured qualitative interview, and whether such simulations are sensitive to variations in trait intensity. Twenty-six adults completed a 29-item open-ended interview and four standardized self-report measures (ASRS, BAARS-IV, AQ, RAADS-R). Two LLMs (GPT-4o and Qwen3-235B-A22B) were prompted to infer an individual psychological profile from interview content and then respond to each questionnaire in-role. Accuracy, reliability, and sensitivity were assessed using group-level comparisons, error metrics, exact-match scoring, and a randomized baseline. Both models outperformed random responses across instruments, with GPT-4o showing higher accuracy and reproducibility. Simulated responses closely matched human data for ASRS, BAARS-IV, and RAADS-R, while the AQ revealed subscale-specific limitations, particularly in Attention to Detail. Overall, the findings indicate that interview-grounded LLMs can produce coherent and above-chance simulations of neurodevelopmental traits, supporting their potential use as synthetic participants in early-stage psychometric research, while highlighting clear domain-specific constraints.
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