Staying Together: A Bidirectional Delay–Coupled Approach to Joint Action

Abstract

To understand how individuals adapt to and anticipate each other in joint tasks, we employ a bidirectional delay–coupled dynamical system that allows for mutual adaptation and anticipation. In delay–coupled systems, anticipation is achieved when one system compares its own time‐delayed behavior, which implicitly includes past information about the other system’s behavior, with the other system’s instantaneous behavior. Applied to joint music performance, the model allows each system to adapt its behavior to the dynamics of the other. Model predictions of asynchrony between two simultaneously produced musical voices were compared with duet pianists’ behavior; each partner performed one voice while auditory feedback perturbations occurred at unpredictable times during live performance. As the model predicted, when auditory feedback from one musical voice was removed, the asynchrony changed: The pianist’s voice that was removed anticipated (preceded) the actions of their partner. When the auditory feedback returned and both musicians could hear each other, they rapidly returned to baseline levels of asynchrony. To understand how the pianists anticipated each other, their performances were fitted by the model to examine change in model parameters (coupling strength, time‐delay). When auditory feedback for one or both voices was removed, the fits showed the expected decrease in coupling strength and time‐delay between the systems. When feedback about the voice(s) returned, the coupling strength and time‐delay returned to baseline. These findings support the idea that when people perform actions together, they do so as a coupled bidirectional anticipatory system.

Behavioral and Neural Dynamics of Interpersonal Synchrony Between Performing Musicians: A Wireless EEG Hyperscanning Study

“…Previously used to detect functional connectivity or coupling within individuals’ brain networks ( Bruns et al, 2000 ; Doron et al, 2012 ; Hipp et al, 2012 ), we applied AECs to detect coupling between individuals. Although the current findings are agnostic with respect to the relationship between phase-resetting mechanisms of endogenous neural oscillations and transient ERPs in response to rhythmic auditory stimuli ( Haegens and Golumbic, 2018 ; Novembre and Iannetti, 2018 ), the measures of enhanced power and amplitude envelopes presented here offer insights into the bidirectional (Leader-Follower) period coupling typical of skilled musical ensembles ( Demos et al, 2019 ).…”

Temporal Coordination in Piano Duet Networked Music Performance (NMP): Interactions Between Acoustic Transmission Latency and Musical Role Asymmetries

“…Interestingly, we also observed that task-related asymmetries, such as dissimilarity between musical parts, may increase the coordinative stability between co-performers. Accordingly, individuals leading music education activities via NMP might aim to prioritize exercises involving complementary, asymmetric musical tasks, especially in cases where ATL is variable or cannot be kept to a minimum around 20 ms. Further experimental research into the effects of ATL on interpersonal coordination during musical performance, including the ongoing development of computational models capturing multi-agent coordination in the context of informational delays (e.g., Rottondi et al, 2016;Demos et al, 2019;Heggli et al, 2019;Román et al, 2019;Shahal et al, 2020), is invaluable to the continued improvement of NMP. Performers have also indicated that physical separation alone poses challenges to music ensemble performance independent of those introduced by ATLs, noting that the associated hindrances to tuning, blending, and taking musical risks can all inhibit creativity (Iorwerth and Knox, 2019).…”

The influence of performing gesture type on interpersonal musical timing, and the role of visual contact and tempo

“…Alternatively, strong anticipatory behavior is fundamentally rooted in the direct (action-perception) coupling of an agent with its environment, forming a dynamical system that is driven towards stable relationships between its components (as for example described by the relative phase in periodic behaviors). In this regard, anticipatory behavior in joint motor coordination may be the outcome of a momentto-moment alignment of actions to perceptual information (Kelso, 1995;Wilson & Golonka, 2013) , of the introduction of small time-delayed self-feedback (Demos, Layeghi, Wanderley, & Palmer, 2019;Roman, Washburn, Large, Chafe, & Fujioka, 2019;Stepp & Turvey, 2010;Washburn, Kallen, Coey, Shockley, & Richardson, 2015) , or of long-term complexity matching of joint behavior (Fine, Likens, Amazeen, & Amazeen, 2015;Marmelat & Delignières, 2012) . These studies provide valuable mathematical methods for the analysis and modeling of complexity and fluctuation structures in joint behavior, with a central focus on nonlinear, time-varying characteristics of this behavior.…”