我们是谁，我们在哪里：心理状况在个体、情境和大语言模型交汇处
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
AI总结：
本文提出了一种结合心理学理论和大语言模型的可解释模型，用于预测幸福感并识别适应性与适应不良的自我状态。
摘要：
心理健康不是固定不变的特质，而是由个体特质与情境背景的相互作用所塑造的动态过程。基于互动论和建构主义心理学理论，我们开发了可解释的模型，以预测幸福感并识别长期社交媒体数据中适应性与适应不良的自我状态。我们的方法整合了个体层面的心理特质（如韧性、认知扭曲、隐性动机）与从情境8 DIAMONDS框架中推导出的语言推断情境特征。我们比较了这些理论指导的特征与一个心理测量学指导的语言模型的嵌入向量，后者捕捉了时间性和个体特定的模式。结果表明，我们的原则性、理论驱动的特征在性能上具有竞争力，同时提供了更大的可解释性。定性分析进一步突显了最能预测幸福感的特征的心理一致性。这些发现强调了将计算建模与心理学理论相结合，以在情境敏感且人类可理解的方式中评估动态心理状态的价值。
英文摘要：
Mental health is not a fixed trait but a dynamic process shaped by the interplay between individual dispositions and situational contexts. Building on interactionist and constructionist psychological theories, we develop interpretable models to predict well-being and identify adaptive and maladaptive self-states in longitudinal social media data. Our approach integrates person-level psychological traits (e.g., resilience, cognitive distortions, implicit motives) with language-inferred situational features derived from the Situational 8 DIAMONDS framework. We compare these theory-grounded features to embeddings from a psychometrically-informed language model that captures temporal and individual-specific patterns. Results show that our principled, theory-driven features provide competitive performance while offering greater interpretability. Qualitative analyses further highlight the psychological coherence of features most predictive of well-being. These findings underscore the value of integrating computational modeling with psychological theory to assess dynamic mental states in contextually sensitive and human-understandable ways.
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。
