TY - GEN
T1 - Predicting Trust Using Automated Assessment of Multivariate Interactional Synchrony
AU - Meynard, Adrien
AU - Seneviratna, Gayan
AU - Doyle, Elliot
AU - Becker, Joyanne
AU - Wu, Hau Tieng
AU - Borg, Jana Schaich
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85125057786&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125057786&partnerID=8YFLogxK
U2 - 10.1109/FG52635.2021.9667082
DO - 10.1109/FG52635.2021.9667082
M3 - Conference contribution
AN - SCOPUS:85125057786
T3 - Proceedings - 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021
BT - Proceedings - 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021
A2 - Struc, Vitomir
A2 - Ivanovska, Marija
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021
Y2 - 15 December 2021 through 18 December 2021
ER -