对治疗师-来访者关系的因果理解:语言形态与社会娱乐的研究。

Alexandria K Vail, Jeffrey M Girard, Lauren M Bylsma, Jeffrey F Cohn, Jay Fournier, Holly A Swartz, Louis-Philippe Morency
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引用次数: 0

摘要

治疗师和客户之间的关系是成功治疗的最关键的决定因素之一。工作联盟是一个多方面的概念,捕捉治疗师与客户关系的合作方面;强大的工作联盟与许多积极的治疗结果广泛相关。虽然治疗过程是决定性的多模态互动,但鉴于其与类似的二元概念(如融洽、合作和隶属关系)的公认关系,语言模态是特别有趣的。具体来说,在这项工作中,我们研究了语言卷入,它衡量了治疗师和客户随着时间的推移对彼此语言使用的适应程度。尽管这一领域的工作越来越多,但是,相对较少的研究考察了人类行为和这些关系指标之间的因果关系:个人对伴侣的感知是否影响他们的说话方式,或者他们的说话方式是否影响他们的感知?我们通过使用结构方程建模(SEM)技术来探索这些问题,该技术允许对治疗师-来访者工作联盟的质量与参与者语言娱乐之间的关系进行多层次和时间建模。在我们的第一个实验中,我们证明了这些技术与其他常见的机器学习模型相比表现良好,并且具有可解释性和因果分析的额外好处。在我们的第二个分析中,我们解释了学习模型来检验工作联盟和语言娱乐之间的关系,并解决了我们的探索性研究问题。结果显示,治疗师的语言夹带对来访者对工作联盟的感知有显著的影响,来访者的语言夹带是他们对工作联盟感知的一个强有力的指标。我们讨论了这些结果的含义,并考虑了未来多模态工作的几个方向。
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Toward Causal Understanding of Therapist-Client Relationships: A Study of Language Modality and Social Entrainment.

The relationship between a therapist and their client is one of the most critical determinants of successful therapy. The working alliance is a multifaceted concept capturing the collaborative aspect of the therapist-client relationship; a strong working alliance has been extensively linked to many positive therapeutic outcomes. Although therapy sessions are decidedly multimodal interactions, the language modality is of particular interest given its recognized relationship to similar dyadic concepts such as rapport, cooperation, and affiliation. Specifically, in this work we study language entrainment, which measures how much the therapist and client adapt toward each other's use of language over time. Despite the growing body of work in this area, however, relatively few studies examine causal relationships between human behavior and these relationship metrics: does an individual's perception of their partner affect how they speak, or does how they speak affect their perception? We explore these questions in this work through the use of structural equation modeling (SEM) techniques, which allow for both multilevel and temporal modeling of the relationship between the quality of the therapist-client working alliance and the participants' language entrainment. In our first experiment, we demonstrate that these techniques perform well in comparison to other common machine learning models, with the added benefits of interpretability and causal analysis. In our second analysis, we interpret the learned models to examine the relationship between working alliance and language entrainment and address our exploratory research questions. The results reveal that a therapist's language entrainment can have a significant impact on the client's perception of the working alliance, and that the client's language entrainment is a strong indicator of their perception of the working alliance. We discuss the implications of these results and consider several directions for future work in multimodality.

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