内部叙事参数化情感状态
Metareasoning constraints couple narratives, affect and cognition
AI总结：
本文通过量化参与者内部叙事的大语言模型表示及其子空间，研究了叙事与情感状态之间的关系，发现特定症状的描述性思维能够预测标准化的抑郁评分，并强调保持症状间的协方差对构建效度至关重要。
摘要：
描述我们如何用语言表达感受对于心理评估和干预至关重要，但叙事与情感状态之间的映射仍然理解不足。在两个大规模研究（n=1257）中，我们通过大语言模型表示及其子空间量化了参与者内部叙事的结构和动态，以参数化抑郁状态。在第一项研究中，我们发现对特定症状的描述性思维捕捉了预测标准化、自我报告抑郁评分的细粒度信息。关键的是，我们显示保持症状之间的特定协方差对于构效效度至关重要，这表明高维文本表示镜像了疾病的潜在几何结构。第二项研究探讨了这种关系的时间动态，当参与者与情感叙事互动时。我们发现量化内部叙事的变化导致自我报告的变化，而基线叙事严重性预测了后续情感变化的幅度。通过将情感视为计算状态，我们的结果强调了其核心、治疗相关功能：约束内部叙事的结构并整合上下文以塑造自我报告。
英文摘要：
Narratives and emotions shape thoughts, and thoughts shape our feelings and stories we tell. Why narrative, affective and cognitive states interact remains unclear. We examine whether this mutual relationship reflects constraints on metareasoning - deciding what to think about - imposed by a shared computational state. Combining self-report and quantification of depression narratives using large language models, Study 1 (n=704) shows narrative state structure closely reflects the factorial structure in formal affect assessments, and that perturbation of the narrative state has commensurate effects on affect via a latent computational state. Study 2 (n=553) uses exposure to structured narratives to test model predictions causally in vivo. Narrative exposure has consistent effect on narrative states, with consequences on momentary mood, cognition, and affect. Critically, effects are predicted by latent computational state engagement. This supports the hypothesis that metareasoning constraints determine interactions between narratives, cognition and affect via a shared computational state.
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。
