EITM: Recognizing, Modeling, and Simulating Coupled Processes in Dyadic Interaction

有大量文献表明，家庭内部关系的质量与家庭成员的身心疾病发生率之间存在对应关系。 这项研究解决了两个基本问题：“家庭成员之间的模式化行为，无论是成年人或成年人与他们的孩子，与关系质量”和“这些模式可以量化到足以在计算机模拟再现？“我们回答这些问题的研究工作分为四个步骤。 首先，我们使用现有的观察数据收集的行为互动的已婚夫妇，以确定哪些行为集群，他们的时间和流量的互动是最相关的关系的质量。 其次，我们然后使用这些提取的行为集来构建模式识别软件（例如，隐马尔可夫模型），以区分从非苦恼的关系。 第三，识别模式，然后成为构建二元互动的计算机模拟的蓝图;我们将开发计算机模拟，再现在苦恼和非苦恼的已婚夫妇中看到的模式序列。 最后，利用现有的数据，我们将应用开发的模式提取和计算机模拟的方法，在亲子互动中观察到的行为。 这项研究的更广泛的影响是双重的：首先，通过利用模式识别技术，我们推进了量化人类互动观察数据的最新技术水平，其次，通过计算机模拟对模式进行分类和测试是开发基于计算机的识别系统的第一步，用于识别高危人群。

There is a substantial literature showing the correspondence between the quality of relationships within a family and the incidence of physical and psychological maladies among its members. This study addresses two fundamental questions: "What patterned behaviors between family members, either adults or adults with their children, are associated with relationship quality" and "Can these patterns be quantified sufficient to be reproduced in computer simulations?" Our research efforts to answer these questions are organized in four steps. First, we use existing observational data collected from the behavioral interactions of married couples to determine which behavioral clusters-their timing and flow-in the interaction are most associated with the quality of the relationship. Second, we then use these extracted behavior sets to construct pattern recognition software (e.g., Hidden Markov Models) to discriminate distressed from non-distressed relationships. Third, the discriminating patterns then become the blueprint for constructing computer simulations of dyadic interaction; we will develop computer simulations that reproduce the sequencing of patterns seen in distressed and non-distressed married couples. Finally, using existing data, we will apply the developed methodology of pattern extraction and computer simulation to behaviors observed in parent-child interactions. The broader impacts of this research are twofold: first, by utilizing pattern recognition technology, we advance the state of the art for quantifying observational data of human interaction, and second, the classification of patterns and testing via computer simulation is the first step in developing a computer-based recognition system for identifying at-risk populations.