Abstract
We introduce Multidimensional Recurrence Quantification Analysis (MdRQA) as a tool to analyze multidimensional time-series data. We show how MdRQA can be used to capture the dynamics of high-dimensional signals, and how MdRQA can be used to assess coupling between two or more variables. In particular, we describe applications of the method in research on joint and collective action, as it provides a coherent analysis framework to systematically investigate dynamics at different group levels—from individual dynamics, to dyadic dynamics, up to global group-level of arbitrary size. The Appendix in Supplementary Material contains a software implementation in MATLAB to calculate MdRQA measures.
Highlights
The interest in joint action research in the past 15 years has come with an increased interest in the temporal dimension of action (Marsh et al, 2009; Knoblich et al, 2011), which offers additional information about linguistic, motor, physiological, or neuro-physiological underpinnings of that behavior
The goal of the present paper is to introduce a multidimensional correlation technique, Multidimensional Recurrence Quantification Analysis (MdRQA), as a method to analyze group-level behavior of groups bigger than a dyad
We show a correlation between group level dynamics of a physiological marker of arousal and independent outcome measures of the joint task
Summary
The interest in joint action research in the past 15 years has come with an increased interest in the temporal dimension of action (Marsh et al, 2009; Knoblich et al, 2011), which offers additional information about linguistic, motor, physiological, or neuro-physiological underpinnings of that behavior Because repetitions are usually never exact, either due to intrinsic fluctuations of the system’s dynamics or measurement noise, a threshold parameter T is applied, within which values in phase-space are counted as being recurrent or not (see Figure 2). MdRQA extends RQA by allowing the use of additional measured variables from the system under study to be used as dimensions in phase-space. Applying RQA directly on multidimensional signals has been done in prior studies on the analysis of joint action by (Mitkidis et al, 2015; Wallot et al, 2016) to quantify the joint dynamics of hand movement in a joint car-model building task, taking each of the four hand acceleration time-series of the collaborating builders as variables
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