SCH: INT: Collaborative Research: Dyadic Behavior Informatics for Psychotherapy Process and Outcome

点击翻译按钮获取中文摘要

Using multimodal indicators, this project will develop a novel computational framework that models individual and interpersonal behavior in relation to process and outcomes in psychotherapy and other interpersonal contexts. The unique aspect of the project is the explicit joint and dyadic modeling of individuals' multimodal behaviors to holistically understand the system of the dyad. This research will pave the way to a better understanding of the dyadic behavior dynamics in psychotherapy and beyond. The project will build the computational foundations to predict process and outcomes, and more broadly inform behavioral science: The project will (1) contribute to knowledge about the psychotherapeutic process by identifying and characterizing behavior indicators with respect to process and outcome measures; (2) deepen our understanding of dyadic coordination dynamics that contribute to strong working alliance between clients and therapists; (3) make available to the research and clinical communities the Dyadic Behavior Informatics framework and Behavior Indicator Knowledgebase for use in other settings; and (4) establish the foundation for novel education and training materials and interventions. The knowledge and computational tools developed as part of this project will impact computing and behavioral science and applied domains more broadly. This project will advance understanding of dyadic behavioral dynamics by developing computational representations that can model fine-grained dyadic coordination between individuals and new algorithms that can model multi-level dynamics. Central to this research effort is the creation of the Behavior Indicator Knowledgebase (BIK) that will summarize discovered knowledge about significant and validated dyadic behavior indicators. While this work could have profound impact on behavioral and social science as a whole, the project specifically focuses on understanding the dynamics that predict process and outcome variables in psychotherapy. The project identifies five fundamental research challenges and presents a plan to address them directly: (1) Acquire a large, dyadic, and multimodal dataset of 64 patients with distress disorders seen over 8 psychotherapy sessions; (2) Create multimodal behavior indicators which can model the within session dynamics of the client or therapist; (3) Develop new dyadic behavior indicators that explicitly model the client-therapist coordination, (4) Develop abstract dyadic behavior representations that can learn the fine-grained dynamics between client and therapist behaviors; and, (5) Validate the computational representations (embeddings) and prediction models by assessing their impact on the predictive power of process and outcome measures in psychotherapy and assess generalizability beyond psychotherapy.