Assessing Multimodal Dynamics in Multi-Party Collaborative Interactions with Multi-Level Vector Autoregression

Abstract

Multi-level vector autoregression (mlVAR) is a recently developed dynamic network model for assessing multimodal temporal data streams derived from multiple users over time. Importantly, mlVAR facilitates investigations into highly complex collaborative interactions within a unified framework. In order to demonstrate the utility of mlVAR for understanding the temporal dynamics of multimodal multi-party (MMP) interactions, we apply it to 9 signals measured from 201 users (67 triads) who engaged in a 15-minute collaborative problem solving task. Measured signals reflect participants' affective states (positive valence and negative valence), physiological states (skin conductance and heart rate), attention (gaze fixation duration and gaze dispersion), nonverbal communication (head acceleration and facial expressiveness), and verbal communication (speech rate). Using node-level metrics of in-strength, out-strength, and synchrony, we show that mlVAR is capable of teasing apart complex role-based dynamics (controller, primary contributor, or secondary contributor) between participants. Our findings also provide evidence for a complex feedback system between individuals where internal states (i.e., skin conductance) are influenced by external signals of shared attention and communication (i.e., gaze and speech).