Triadic Synchrony: Application of Multiple Wavelet Coherence to a Small Group Conversation

Abstract

By applying multiple wavelet coherence (MWC) to data from human body movements in triadic interaction, this study quantified triadic synchrony, rhythmic similarity among three interactants. Thirty-nine Japanese undergraduates were randomly assigned in a triad, and engaged in a brainstorming task. Triadic synchrony was quantified by calculating MWC to the time-series movement data collected by Kinect v2 sensor. The existence of synchrony was statistically tested by using a pseudo-synchrony paradigm. Results showed that the averaged value of MWC was higher in the experimental participant trio than in those of the pseudo trio in the frequency band of 0.5 -1 Hz. The result supports the possible utility of applying multiple wavelet coherence to evaluate triadic synchrony in a small group interaction.

Synchrony as a measure of conversation difficulty: Movement coherence increases with background noise level and complexity in dyads and triads

“…In all cases, we found greater synchrony in more difficult communication conditions, as hypothesised. This is a novel investigation of synchrony in conversational groups of different sizes, and is one of relatively small number of studies of triadic groups [ 51 , 55 ]. We consider the detail of these effects in turn below.…”

“…We use cross wavelet coherence analysis to evaluate the amount of head movement synchrony within dyads (and within the constituent pairs of triads). Typically used to analyse long-term timeseries in geophysics [ 38 ], wavelet coherence has recently been used to study conversational synchrony in dyads [ 11 , 12 ] and in triads [ 51 ]. Wavelet coherence reveals the amount of time and frequency-domain correlation between two signals, regardless of the power in the different frequency components.…”

“…Wavelet coherence is well defined as a pairwise measure of synchrony, however it is less well defined how it might be adapted for three or more signals. An algorithm based on multiple wavelet coherence [ 52 ] was previously used on triadic conversations [ 51 ]. However, that approach produces different results depending on which signal is chosen as the ‘dependent’ signal [ 52 ].…”

Negative emotions disrupt intentional synchronization during group sensorimotor interaction.

“…Moreover, the group-level analysis provides a framework for empirical research into the effect of collective effervescence and the group-related tasks that are otherwise not accessible at the dyad level (Keltner & Haidt, 1999). This rationale gives rise to a relatively new research trend of group synchronization (e.g., Bieńkiewicz et al, 2023; Dale et al, 2020; Fujiwara, 2016; Tomashin et al, 2022), which we follow in this article.…”

“…Content may be shared at no cost, but any requests to reuse this content in part or whole must go through the American Psychological Association. Fujiwara, 2016;Tomashin et al, 2022), which we follow in this article.…”