COMPASS 2.0：心理测量表征相似性分析区分症状结构与个人信号
COMPASS 2.0: psychometric representational similarity analysis distinguishes symptom structure from personal signal
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中文总结 AI 辅助
本文提出心理测量表征相似性分析框架，在COMPASS 2.0中实现，用于区分语言模型评分反映的问卷措辞结构与真实个人信号，并通过275名参与者的预注册分析证明症状几何主要反映措辞而非自我报告。
中文摘要 AI 辅助
语言模型可以从言语中为精神病学问卷评分，但与自我报告的一致性可能反映的是问卷本身而非个体。我们引入了心理测量表征相似性分析，这是一个用于比较言语派生评分、自我报告、项目措辞和理论结构的框架，并将其与个体层面构念评分一起在COMPASS 2.0中实现。我们展示了相似措辞的项目如何在没有心理信号的情况下诱发协方差。在对275名参与者的临床访谈进行的预注册发现和确认分析中，语言派生的症状几何结构与措辞的相似度高于自我报告，注册测试未检测到措辞之外的结构。与自我报告的几何一致性在将参与者分配给他人的答案后仍然存在，而个体配对评分捕获的痛苦多于特定症状。补充分析检查了34种工具和研究领域标准（RDoC）框架的咨询质量和措辞结构。这些发现区分了关于心理结构的一致性与语言派生评估追踪个体的证据。
英文摘要
Language models can score psychiatric questionnaires from speech, but agreement with self-report may reflect the questionnaire rather than the person. We introduce psychometric representational similarity analysis, a framework for comparing the structure of speech-derived scores, self-report, item wording and theory, and implement it alongside person-level construct scoring in COMPASS 2.0. We show how similarly worded items induce covariance without psychological signal. In pre-registered discovery and confirmation analyses of clinical interviews from 275 participants, language-derived symptom geometry resembled wording more than self-report, with no structure beyond wording detected by the registered tests. Geometric agreement with self-report survived assigning participants someone else's answers, whereas person-paired scores captured distress more than specific symptoms. Complementary analyses examined counselling quality and wording structure across 34 instruments and the Research Domain Criteria (RDoC) framework. These findings distinguish agreement about psychological structure from evidence that language-derived assessments track individual people.
发表机构
- Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
- James J. Peters VA Medical Center（詹姆斯·J·彼得斯退伍军人事务医疗中心）
- Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。
