Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Abstract

Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g., behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g., mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e., scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

The pivotal role of monitoring for collaborative problem solving seen in interaction, performance, and interpersonal physiology

“…For example, monitoring with different valence characteristics triggers different types of feedback loops of regulation, which are not linear but are likely to greatly affect the following learning process (Azevedo 2014). It is also important to investigate the temporal changes of interpersonal physiology such as moving in and out of synchrony (Likens and Wiltshire 2020), because these seem to be prominent in revealing the quality of the collaboration (Schneider et al 2020) and might reflect adaptation or mal-adaptation of a group (Saxbe et al 2020;Sobocinski et al 2020). Because regulation in collaborative learning is a dynamic process and emerges on different systemic levels, which are likely to constantly interact with each other (Reimann 2019;Volet et al 2009), a complex dynamical systems approach might offer potential methodological tools (e.g., MdRQA) for researching it in the future (Hilpert and Marchand 2018;Jacobson et al 2016).…”

Collaborative Creativity: Information-driven coordination dynamics and prediction in movement and musical improvisation

“…One assumption that we make here is that collaborative creativity requires a shared goal (i.e., to create together) and as such, we distinguish this from other forms of emergent coordination, such as spontaneous entrainment (Dumas & Fairhurst, 2019;Knoblich et al, 2011). How these two types of coordination types vary in terms of more fine scale dynamics is not yet well detailed as current computational approaches typically focus on quantifying overall coordination (cf., Likens & Wiltshire, 2020;Wiltshire et al, 2019). Regardless, shared goals require interacting agents, like a group of jazz musicians, to both anticipate actions and respond to each other in meaningful ways so as to coordinate their behavior (Phillips-Silver & Keller, 2012;Wöllner, 2020).…”

“…Interpersonal coordination as an area of inquiry is growing and advancing our understanding of which modalities (e.g., physiology, kinematics, social behavior, language, etc.) we coordinate with others in (Delaherche et al, 2012;Palumbo et al, 2017;Wiltshire et al, 2020), what mechanisms make this coordination possible (Hoehl et al, 2020;Koole & Tschacher, 2016), how we move in and out of synchrony with others (Likens & Wiltshire, 2020;Mayo & Gordon, 2020;Wiltshire et al, 2019), and what the interactional benefits of coordination are (Rennung & Göritz, 2016). In cases of joint action, coordination can be emergent or planned and would subsequently draw on a variety of perceptuo-motor and/or cognitive processes (see Knoblich et al, 2011 for a detailed review).…”

Being ‘in sync’—is interactional synchrony the key to understanding the social brain?

“…Moreover, current approaches look at synchrony in a fairly superficial manner by, for example, comparing the mean values obtained in one condition to those from another condition. However, like its underlying time series measures, synchrony is a dynamical phenomenon that likely waxes and wanes, breaks and resets ( Likens and Wiltshire (n.d) ). Hence, moments of low or no synchrony may be as or perhaps more important than those with high synchrony.…”