Physiological evidence of interpersonal dynamics in a cooperative production task

Abstract

Recent research suggests that shared behavioral dynamics during interpersonal interaction are indicative of subjective and objective outcomes of the interaction, such as feelings of rapport and success of performance. The role of shared physiological dynamics to quantify interpersonal interaction, however, has received comparativley little attention. In the present study, we investigate the coordination dynamics of multiple psychophysiological measures and their utility in capturing emotional dynamics in teams. We use data from an experiment where teams of three people built origami boats together in an assembly-line manner while their heart rate, skin conductance, and facial muscle activity were recorded. Our results show that physiological synchrony of skin conductance measures and eletromyographic measures of the corrugator supercilii develops spontaneously among team members during this cooperative production task. Moreover, high team synchrony is found indicative of team cohesion, while low team synchrony is found indicative of a teams' decision to adopt a new behavior across multiple production sessions. We conclude that team-level measures of synchrony offer new and complementary information compared to measures of individual levels of physiological activity.

Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action

“…We show a correlation between group level dynamics of a physiological marker of arousal and independent outcome measures of the joint task. In accordance with previous analysis of the experiment using different techniques, this could not be seen at the level of aggregate individuals (Håkonsson et al, 2015) or dyads (Mønster et al, 2016a). Finally, we will end the article by discussing the interpretation of MdRQA results for group-level analysis, and summarize the advantages, disadvantages, and potential future developments of this technique.…”

“…In the following, we will apply MdRQA to empirical data to demonstrate how it can be used to systematically analyze group dynamics at different levels of aggregation: individuals, dyads, and at a global group level. In order to do so, we present a re-analysis of a sub-set of data from a study on teamwork investigating the role of team emotions for cooperation (Håkonsson et al, 2015; Mønster et al, 2016a). …”

“…In the following sections, we will describe MdRQA, explain its relation to standard Recurrence Quantification Analysis of individual time-series (RQA) and Cross-Recurrence Quantification Analysis of pairs of time-series (CRQA)—both of which have already been used to analyze dynamics of dyadic behavior (Shockley et al, 2003; Richardson and Dale, 2005; Richardson D. C. et al, 2007; Richardson M. J. et al, 2007; Konvalinka et al, 2011; Louwerse et al, 2012; Lang et al, 2016; Mønster et al, 2016a; Fusaroli and Tylén, 2016). We will also compare MdRQA to Joint Recurrence Quantification Analysis (JRQA)—another recurrence method that can be used to jointly analyze two or more time series.…”

Recurrence Quantification Analysis of Processes and Products of Discourse: A Tutorial in R

“…There is an increasing body of research in psychology that used RQA or its variants to investigate such different topics as motor control and coordination (Abney, Warlaumont, Haussman, Ross, & Wallot, 2014;Farina, Fattorini, Felici, & Filligoi, 2002;Kuznetsov & Riley, 2010;Rhea & Kiefer, 2013;Riley & Turvey, 2002;Riley, Balasubramaniam, & Turvey, 1999), joint action (Mønster, Håkonsson, Eskildsen, & Wallot, 2016;Ramenzoni, Davis, Riley, Shockley, & Baker, 2011;Shockley, Santana, & Fowler, 2003), and conversation (Dale & Spivey, 2006;Fusaroli & Tylén, 2016;Louwerse, Dale Bard, & Jeuniaux, 2012;Richardson, Dale, & Kirkham, 2007). However, applications to more classical cognitive tasks and research questions are less developed.…”

Studying Complex Interactions in Real Time: An XMPP-based Framework for Behavioral Experiments

“…Recent studies of interacting groups suggest that part of the key to understanding successful collaboration is connected to the dynamics of how group members interact in real time (Mønster et al, 2016;Wallot et al, 2016;Fusaroli and Tylén, 2016). Another recent study indicates that real-time interaction influences the efficiency and fairness of collective outcomes (Hawkins and Goldstone, 2016).…”