Challenges for the understanding of the dynamics of social coordination

Abstract

The way people interact can be examined by looking at the way they move relative to each other. Seeking the principles behind those interactions have consequences potentially related to any type of interpersonal function, far beyond the so-called “motor” processes typically associated with the study of movements, be it perceptive, cognitive, affective, pragmatic, or epistemic. Here, we present the way the framework of coordination dynamics define and addresses the interactive actions in a dyad. We first introduce the basics of pattern formation as the roots of the theoretical approach of coordination dynamics, and then the way this framework may contribute to establish a solution to classify behaviors. Thereafter we review promising empirical results on the dynamics of interpersonal coordination, and finally discuss were to go next to decipher the way the coordination between two people and the way each individual contribute may be disentangled.

Embodiment of intersubjective time: relational dynamics as attractors in the temporal coordination of interpersonal behaviors and experiences

“…In the light of the previous sections, we would expect that temporal coordination of behaviors emerges spontaneously from the self-organization of between-persons relational dynamics. This hypothesis is supported by numerous studies (for reviews, see Oullier and Kelso, 2009; Delaherche et al, 2012; Schmidt et al, 2012; Dale et al, 2013; Lagarde, 2013). For instance, when pairs of participants oscillate their legs in anti-phase (opposite directions) at an increasing frequency, their coupling becomes unstable near to a critical threshold; phase wandering between attractors or abrupt transitions toward more stable patterns is observed (Schmidt et al, 1990), a typical signature of self-organized dynamical systems that are modeled by non-linearly coupled oscillators (see also Schmidt and Turvey, 1994; Amazeen et al, 1995).…”

Self-organized complementary joint action: Behavioral dynamics of an interpersonal collision-avoidance task.

“…In conclusion, the current findings provide evidence that the dynamical coordination processes previously known to underlie incidental motor synchronization can also support more complex goal-directed joint action, and that complementary action roles can naturally emerge during ongoing joint action behavior. The current findings also highlight how complementary joint actions are functionally related to the spatiotemporal symmetry of interagent coupling, as well as movement and task constraints, and that understanding the symmetries that define a joint action task space is fundamental to understanding the dynamic order of complex joint action behavior (Lagarde, 2013). Indeed, the dynamical processes that support complex joint action behavior may well be characterized by the induction and maintenance of asymmetric interagent relations, with changes in the symmetry of joint action behavior marking the emergence of higher or lower orders of complex and complementary social activity and coordination.…”

To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task

“…In turn, a growing number of researchers have also argued that multiagent activity is best conceptualized as a complex dynamical system and, moreover, that the behavioral order of self-organized, synergistic multiagent coordination can be understood and modeled using low-dimensional task or behavioral dynamics principles (e.g., Schmidt et al, 1990, 1998; Warren, 2006; Lagarde, 2013; Dumas et al, 2014; Richardson and Kallen, 2015; Richardson et al, 2015). …”

“…Interestingly, previous research investigating the dynamical processes of coordinated joint-action and multiagent activity has demonstrated that the behavioral order of such activity is often self-organized and synergistic , naturally emerging from the task-relevant physical, biomechanical, and informational couplings and constraints that exist between co-actors and within a joint-action task space (e.g., Schmidt et al, 1990 , 2012 ; Schmidt and O'Brien, 1997 ; Marsh et al, 2006 ; Frank and Richardson, 2010 ; Richardson et al, 2010 ; Riley et al, 2011 ; Anderson et al, 2012 ; Richardson and Kallen, 2015 ; Washburn et al, 2015 ). In turn, a growing number of researchers have also argued that multiagent activity is best conceptualized as a complex dynamical system and, moreover, that the behavioral order of self-organized, synergistic multiagent coordination can be understood and modeled using low-dimensional task or behavioral dynamics principles (e.g., Schmidt et al, 1990 , 1998 ; Warren, 2006 ; Lagarde, 2013 ; Dumas et al, 2014 ; Richardson and Kallen, 2015 ; Richardson et al, 2015 ).…”