论文标题
使用多元互动同步的自动评估来预测信任
Predicting Trust Using Automated Assessment of Multivariate Interactional Synchrony
论文作者
论文摘要
多样化的学科对互动代理人随着时间的流动，情感和生理学的协调如何影响社会行为感兴趣。在这里，我们描述了一种新的多元过程，用于自动化这种与行为相关的“互动同步”的研究，并根据动态时间扭曲（DTW）路径的特征引入一种新型的交互性同步度量。我们证明，我们基于DTW的基于DTW路径的互动同步度量可以使用两个人的面部动作单元之间自由互动的自由社会互动互动，以预测他们在随后的信任游戏中将显示多少信任。我们还表明，我们的方法的表现优于单变量的头部运动模型，即独立考虑参与者的面部动作单元的模型以及使用先前提出的同步或相似性度量的模型。这项工作的见解可以应用于旨在量化多个信号随着时间的时间协调的任何研究问题，但在心理学，医学和机器人技术中有直接应用。
Diverse disciplines are interested in how the coordination of interacting agents' movements, emotions, and physiology over time impacts social behavior. Here, we describe a new multivariate procedure for automating the investigation of this kind of behaviorally-relevant "interactional synchrony", and introduce a novel interactional synchrony measure based on features of dynamic time warping (DTW) paths. We demonstrate that our DTW path-based measure of interactional synchrony between facial action units of two people interacting freely in a natural social interaction can be used to predict how much trust they will display in a subsequent Trust Game. We also show that our approach outperforms univariate head movement models, models that consider participants' facial action units independently, and models that use previously proposed synchrony or similarity measures. The insights of this work can be applied to any research question that aims to quantify the temporal coordination of multiple signals over time, but has immediate applications in psychology, medicine, and robotics.
