从提示到构念：面向心理学研究的大语言模型双效度框架
From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology
AI总结：
本研究针对大语言模型应用于心理学研究时的测量幻影问题，构建了双效度框架，明确不同研究目标对应的证据要求，强调需开发心理构念的计算类似物而非直接套用人类测量工具。
摘要：
大语言模型（LLMs）正以工具和研究对象的双重身份进入心理学研究领域。然而，诸多研究将人类测量工具应用于LLMs，却未验证其输出的可靠性或可解释性，这催生了“测量幻影”的风险——即把统计规律性误判为真实的心理现象。本综述指出，稳健的AI心理学研究需整合两大方法论传统：一是对分数含义的心理测量学验证，二是对结果可支撑结论的因果推断标准。该研究构建了双效度框架，其中证据要求随科学目标的不同而调整：从工具使用、行为表征、人类模拟到认知建模，分类文本仅需准确性与可靠性；而声称LLM能模拟焦虑或阐明认知机制，则需额外证据，包括构念效度证据与实验控制。研究进展依赖于开发心理构念的计算类似物，而非假设人类测量工具可自动适用于语言模型。
英文摘要：
Large language models (LLMs) are entering psychological research both as tools and as objects of inquiry. Yet many studies apply human instruments to LLMs without establishing that the outputs are reliable or interpretable, raising the risk of measurement phantoms--statistical regularities mistaken for genuine psychological phenomena. This review argues that robust AI psychological research requires integrating two methodological traditions: psychometric validation of what a score means and causal inference standards for what the results warrant. It develops a dual-validity framework in which evidentiary demands scale with scientific ambition: from tool use through behavioral characterization and human simulation to cognitive modeling. Classifying text may require only accuracy and reliability; claiming that an LLM simulates anxiety or illuminates cognitive mechanisms requires additional evidence, including construct validity evidence and experimental controls. Progress depends on developing computational analogs of psychological constructs rather than assuming human measures automatically apply to language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
