Measuring group synchrony: a cluster-phase method for analyzing multivariate movement time-series

Abstract

A new method for assessing group synchrony is introduced as being potentially useful for objectively determining degree of group cohesiveness or entitativity. The cluster-phase method of Frank and Richardson (2010) was used to analyze movement data from the rocking chair movements of six-member groups who rocked their chairs while seated in a circle facing the center. In some trials group members had no information about others' movements (their eyes were shut) or they had their eyes open and gazed at a marker in the center of the group. As predicted, the group level synchrony measure was able to distinguish between situations where synchrony would have been possible and situations where it would be impossible. Moreover, other aspects of the analysis illustrated how the cluster phase measures can be used to determine the type of patterning of group synchrony, and, when integrated with multi-level modeling, can be used to examine individual-level differences in synchrony and dyadic level synchrony as well.

Entrainment and task co-representation effects for discrete and continuous action sequences

“…To do so, we used the cluster-phase method (Frank & Richardson, 2010;Richardson, Garcia, Frank, Gergor, & Marsh, 2012). This method quantifies rho (a measure of group synchrony Fig.…”

Influences on and Measures of Unintentional Group Synchrony

“…In a laboratory setting with minimal interaction, Richardson et al (2012) examined if group synchrony could be facilitated by mutual information among individuals. Groups of six participants each rocked chairs while seated in a circle facing the center, either with their eyes closed, which would have captured chance coordination, or with their eyes open, which provided information about others’ actions.…”

“…However, while a large body of research has attempted to examine and quantify unintentional and intentional dyadic synchrony (see Marsh et al, 2009; Oullier and Kelso, 2009 for reviews), it remains unclear if or how such investigations and measures could be generalized to the study of group synchrony (Richardson et al, 2012), which may underlie other important social processes such as group cohesiveness (Campbell, 1958) and group decision making (Janis, 1982). In addition, while joint social activities often require individuals to intentionally or deliberately coordinate their behaviors to achieve a shared goal (see Keller et al, 2014; Elliott et al, 2016 for reviews), many social interactions that do not instruct individuals to align their behaviors also frequently lead to unintentional or spontaneous coordination (Richardson et al, 2007; Lakens, 2010). In contrast to intentional synchrony, which tends to override the intrinsic dynamics or rhythms of individuals’ behaviors, unintentional synchrony enables the assessment of the factors that constrain and maintain coordination among people’s movements or responses during unstructured interactions (Richardson et al, 2007; Lakens, 2010).…”

“…In addition, while joint social activities often require individuals to intentionally or deliberately coordinate their behaviors to achieve a shared goal (see Keller et al, 2014; Elliott et al, 2016 for reviews), many social interactions that do not instruct individuals to align their behaviors also frequently lead to unintentional or spontaneous coordination (Richardson et al, 2007; Lakens, 2010). In contrast to intentional synchrony, which tends to override the intrinsic dynamics or rhythms of individuals’ behaviors, unintentional synchrony enables the assessment of the factors that constrain and maintain coordination among people’s movements or responses during unstructured interactions (Richardson et al, 2007; Lakens, 2010). …”

Fractal Analyses: Statistical And Methodological Innovations And Best Practices

“…Gao et al (2012) introduces a very general analysis that is suitable for use on both stochastic and continuous measurements. Finally, Richardson et al (2012) describe techniques that assess relative dynamic synchrony among multiple coupled oscillatory time-series. Taken together, the chapters offer a gamut of analytic strategies alongside contemporary expertise on how to best conduct and interpret the outcomes of fractal analyses.…”