  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.
