Categorical Cross‐Recurrence Quantification Analysis Applied to Communicative Interaction during Ainsworth’s Strange Situation

Abstract

The goal of this study was to characterize the degree of structuring of verbal and motor behaviours, unfolded during the application of an procedure called the Strange Situation. This procedure is used for assessing children’s attachment quality during early stages of their development. Many studies have demonstrated that communicative interactions share features with complex dynamic systems. In such studies, estimations of degree of structure have been used to characterize the system’s synchronization. Thus, assuming that processes of communicative interaction occur in the Strange Situation procedure, it was expected to find traces of synchronization. The metrics were estimated through a Categorical Cross-Recurrence Quantification Analysis applied to the behaviours of individuals and dyads. Two applications of the Strange Situation were implemented and recorded. Verbal and motor interactions among children, caregivers, and strangers were transcribed, categorized, and organized as time series. From each time series of original behaviours, randomized time series were created. Measures of recurrence extracted from Recurrent Plots, such as determinism, entropy, maximum line, laminarity, and trapping time, were calculated. Original and randomized time series were compared in terms of these measures. Results indicated that communicative interaction during the Strange Situation had a structure that mimics properties observed in social interactions where synchronization emerges. In our case, verbal behaviours were more prone to synchronization than motor behaviours, in both individuals and dyads, even though this pattern was more salient among caregivers and strangers than children. The relevance of having measures that can capture synchronization during the administration of the Strange Situation is discussed. Our preliminary findings allow us to point out that the application of RQA and C-RQA to the Strange Situation could not only contribute to methodology, but also contribute to emphasizing the role of coupling in communicative interaction generated by the application of this procedure to measure attachment patterns.

Parliamentary roll-call voting as a complex dynamical system: The case of Chile

“…To compare the results without the effect of the time structure, the order of the original series was randomized 30 times [ 15 , 19 ]. Then, the non-linear analyses were performed for each randomization (RQA and SE).…”

“…We performed the RQA (taking the recurrence rate and determinism) and SE on these periods for both variables. Subsequently, we randomized the order of all points in the time series in each period 30 times (i.e., random shuffling) [15,19]. We performed the same analyses to compare PLOS ONE the resulting measures with those of the original time series.…”

“…If we treat legislative votes as a time series, where each vote is a time point, we can use an analysis method called Recurrence Quantification Analysis (RQA). RQA is a non-linear method used to discover tenuous correlations and repetitive patterns in a time series [ 15 ]. Unlike other analyses, it does not require additional treatment or assumptions about data distribution [ 16 – 18 ].…”

“…RQA enables us to capture the dynamic organization of such systems based on the amount and patterns of such recurrences in phase space. This technique has been used in various domains to characterize temporal patterns of human and non-human behavior [15,[19][20][21]. However, despite its use in various disciplines, it has been scarcely used in analyzing political variables.…”

The nonlinearity of pupil diameter fluctuations in an insight task as criteria for detecting children who solve the problem from those who do not

“…Based on the analyses conducted by Dixon and colleagues, we utilized two statistical techniques to examine pupil diameter fluctuations: Power Spectrum Density (PSD) for estimating power-law scaling (Hausdorff et al, 1995;Peng et al, 1995;Holden, 2005;Kello et al, 2007;Wijnants et al 2012a,b); and Recurrence Quantification Analysis (RQA) for estimating entropy, recurrence ratio, and determinism (Marwan and Kurths, 2002;Shockley, 2005;Orsucci et al, 2006;Marwan et al 2007;Bastos and Caiado, 2010;Ngamga et al, 2016;Lira-Palma et al, 2018;Morales-Bader et al, 2023).…”