From Dyads to Networks: A Round‐Robin Approach to Physiological Synchrony

This is the authors' abstract. We don't add key points for this paper.

Physiological synchrony has been studied both at the dyadic level, where pairs are examined in isolation, and at the group level, where the collective is often characterized by a single index. Yet neither approach captures the full relational architecture of synchrony within a group. Here we propose a framework combining a sequential round‐robin design, in which each group member interacts one‐on‐one with every other member, with Small World Network (SWN) analysis. Although synchrony is measured as a dyadic property of each pair, this design permits characterization at three levels simultaneously: the individual level, who synchronizes consistently across partners; the dyadic level, which pairs synchronize more; and the group level, the overall architecture of coupling. Physiological synchrony (e.g., breathing, heart rate, electrodermal activity and even body odor) can be quantified for each dyad and used to construct a weighted synchrony network, where nodes represent individuals and edges represent the degree of physiological coupling. To leverage dyadic measures in order to characterize the network‐level topology of physiological synchrony, we suggest applying graph theory measures such as clustering coefficients, average shortest path length, density, degree distribution, and modularity. This enables answering novel questions: whether synchrony is transitive, whether it shows small‐world properties, and whether some individuals act as hubs, serving as “super synchronizers”. If there are “super synchronizers”, is this due to their interpersonal adaptability, the typicality of their physiological signals, or both? We present a framework for uncovering the multi‐level structure of physiological synchrony that emerges when individuals are observed across multiple dyadic interactions within a session. We illustrate our suggestions using an existing sequential round‐robin dataset (two sessions, 66 dyads), with body odor as the physiological measure and inclusion of the other in the self, a measure for perceived self‐other overlap and interpersonal closeness, as the outcome.