Quantifying synchronization in groups with three or more members using SyncCalc: The driver-empath model of group dynamics.

Abstract

Objectives: This article introduces a nonlinear dynamical systems model to quantify synchronization among group members with regard to physiological activity or overt behaviors. Method: The driver-empath model accommodates asymmetries in influence among group members, separates autocorrelation effects from synchronization with other group members in time series analysis, accommodates dynamics that could be more complex than simple oscillators, and produces metrics at the individual, dyadic, and group levels of analysis. Results: An illustrative example of a team of seven undergraduates in a group decision-making task is presented with commentary for data preparation and determining lag length of time series data. The supporting literature summarizes connections between the group-level synchronization coefficient and group-level workload, cooperation versus competition dynamics, leadership emergence, and team performance. Conclusions: The driver-empath model has accumulated substantial external validity for answering research questions in group dynamics. Suggested new applications include studying synchrony across different network configurations and possible social fault lines.

Introduction to special issue: Data analytic methods in group psychology and group psychotherapy.

“…Next, Guastello and Peressini (2023) introduce the driver-empath model, a nonlinear dynamical systems model, to quantify synchronization among group members with regard to physiological activity or overt behaviors. The driver-empath model accommodates asymmetries in influence among group members, separates autocorrelation effects from synchronization with other group members in time series analysis, accommodates dynamics that could be more complex than simple oscillators, and produces metrics at the individual, dyadic, and group levels of analysis.…”

Examining Physiological Features Underlying Team Coordination Breakdowns

“…Computational identi cation of TCBs was based on the EDA and PPG signals of team members. To these signals, the researchers applied two different continuous measures of coordination: windowed synchronization coe cient 41,42 Transitions in the resulting coordination were located through a combination of change point and nonlinear prediction algorithms. The change point algorithm identi ed transitions, through the minimization of a given cost function over possible amounts and locations of change points within a time series [45][46][47] .…”