Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R

Abstract

This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a recognition that many behavioral and neurophysiological processes are intrinsically time dependent and reliant on environmental and social context has emerged. Recurrence-based methods are particularly suited for time-series that are non-stationary or have complicated dynamics, such as longer recordings of continuous physiological or movement data, but are also useful in the case of time-series of symbolic data, as in the case of text/verbal transcriptions or categorically coded behaviors. In the past, they have been used to assess changes in the dynamics of, or coupling between physiological and behavioral measures, for example in joint action research to determine the co-evolution of the behavior between individuals in dyads or groups, or for assessing the strength of coupling/correlation between two or more time-series. In this paper, we provide readers with a conceptual introduction, followed by a step-by-step explanation on how the analyses are performed in R with a summary of the current best practices of their application.

Physiological Synchrony Predicts Observational Threat Learning in Humans

“…2, Methods). CRQA quantifies the similarity of two signals and is suitable for complex non-stationary signals where non-linear dyanamics may exist [27,34,40,41]. We first constructed cross-recurrence plots for each dyad's phasic skin conductance time series from the learning phase portion of each block (see Fig.…”

“…While the preceding analyses indicated strong support for our hypothesis that interpersonal synchrony during observational learning predicts later threat responses, it does not show that this effect is specific to the actual demonstrator-observer dyads from our experiment. To address this issue we followed existing recommendations [31,35,40], and created random permutations of our data by pairing participants' across dyad boundaries. These pairings can be thought of as pseudo-dyads.…”

What does physiological synchrony reveal about metacognitive experiences and group performance?

“…Third, signals were standardised with the aim to make them comparable with each other (Ben‐Shakhar, 1985). Standardisation has been successfully used for EDA (Ben‐Shakhar, 1985; Dawson et al ., 2017) and is also suggested for MdRQA analysis to ensure that its measures are based on sequential similarity of the time‐series’ (Wallot & Leonardi, 2018). Fourth, phasic signal component was derived from the signal with Ledalab continuous decomposition analysis including adaptive smoothing of the signal (Benedek & Kaernbach, 2010).…”

“…data such as EDA (Wallot & Leonardi, 2018; Wallot, Roepstorff, & Mønster, 2016). It has become a prominent technique for calculating temporal interdependence among the physiological signals of individuals doing collaborative work (Dindar et al ., (2019); Wallot, Mitkidis, et al ., 2016).…”

Recurrence Methods for Communication Data, Reflecting on 20 Years of Progress

“…A summary and synthesis of many of the aforementioned approaches was offered by Leonardi [27]. In addition, the availability of practical instruction on the design and implementation of recurrence analyses of language data have also been made available by Wallot [28] and Wallot and Leonardi [29].…”