Predicting Trust Using Automated Assessment of Multivariate Interactional Synchrony

TL;DR

This paper introduces a new multivariate method using dynamic time warping to automatically measure interactional synchrony, which can predict trust in social interactions and outperforms previous models.

Contribution

The authors develop a novel DTW-based interactional synchrony measure that improves prediction of trust and surpasses existing univariate and similarity-based models.

Findings

DTW path-based synchrony measure predicts trust levels.

The approach outperforms univariate and existing synchrony models.

Applicable across psychology, medicine, and robotics domains.

Abstract

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.