Psychotherapy is a primary treatment for many mental health conditions, yet the interplay among therapist behaviors, client responses, and the therapeutic relationship remains difficult to untangle. This work advances a computational approach for modeling these moment-to-moment processes. We first developed automated methods using large language models (LLMs) to assess therapist behaviors (e.g., empathy, exploration), relational qualities (e.g., rapport), and client outcomes (e.g., disclosure, self-directed and outward-directed negative emotions). These measures showed strong alignment with human ratings (mean Pearson $r = .66$). We then analyzed nearly 2,000 hours of psychotherapy transcripts from the Alexander Street corpus using Structural Equation Modeling (SEM). SEM showed that therapist empathy and exploration directly shaped client disclosure and emotional expression, whereas rapport may contribute to reductions in internal emotional distress rather than increased willingness to express it. Together, these findings demonstrate how computational tools can capture core therapeutic processes at scale and offer new opportunities for understanding, modeling, and improving therapist training.

翻译：心理治疗是许多心理健康问题的主要治疗手段，然而治疗师行为、来访者反应与治疗关系之间的相互作用机制仍难以厘清。本研究提出了一种计算建模方法来刻画这些即时动态过程。我们首先开发了基于大语言模型（LLMs）的自动化评估方法，用于测量治疗师行为（如共情、探索）、关系质量（如融洽关系）以及来访者结果（如自我表露、自我指向与他人指向的负性情绪）。这些测量指标与人工评分高度吻合（平均皮尔逊相关系数 $r = .66$）。随后，我们使用结构方程模型（SEM）对亚历山大街语料库中近2000小时的心理治疗转录文本进行分析。SEM分析表明：治疗师的共情与探索行为直接影响来访者的自我表露与情绪表达，而融洽关系可能主要促进内部情绪困扰的缓解而非增强情绪表达的意愿。综合而言，这些发现证明了计算工具如何能够大规模捕捉核心治疗过程，并为理解、建模和改进治疗师培训提供了新的机遇。