Nominal cross recurrence as a generalized lag sequential analysis for behavioral streams

We briefly present lag sequential analysis for behavioral streams, a commonly used method in psychology for quantifying the relationships between two nominal time series. Cross recurrence quantification analysis (CRQA) is shown as an extension of this technique, and we exemplify this nominal application of CRQA to eye-movement data in human interaction. In addition, we demonstrate nominal CRQA in a simple coupled logistic map simulation used in previous communication research, permitting the investigation of properties of nonlinear systems such as bifurcation and onset to chaos, even in the streams obtained by coarse-graining a coupled nonlinear model. We end with a summary of the importance of CRQA for exploring the relationship between two behavioral streams, and review a recent theoretical trend in the cognitive sciences that would be usefully informed by this and similar nonlinear methods. We hope this work will encourage scientists interested in general properties of complex, nonlinear dynamical systems to apply emerging methods to coarse-grained, nominal units of measure, as there is an immediate need for their application in the psychological domain

Quantifying Joint Activities using Cross-Recurrence Block Representation

Humans, as social beings, are capable of employing variousbehavioral cues, such as gaze, speech, manual action, and bodyposture, in everyday communication. However, to extract fine-grained interaction patterns in social contexts has beenpresented with methodological challenges. Cross-RecurrencePlot Quantification Analysis (CRQA) is an analysis methodinvented in theoretical physics and recently applied tocognitive science to study interpersonal coordination. In thispaper, we extend this approach to analyzing joint activities inchild-parent interaction. We define a new representation asCross Recurrence Block based on CRQA. With thisrepresentation, we are able to capture interpersonal dynamicsfrom more than two behavioral streams in one CrossRecurrence Plot and derive a suite of measures to quantifydetailed characteristics of coordination. Using a datasetcollected from a child-parent interaction study, we show thatthese quantitative measures of joint activities revealdevelopmental changes in coordinative behavioral patternsbetween children and parents

Unravelling cross-recurrence: coupling across timescales

We present an extension of cross-recurrence analysis for dual gaze analysis which is suited for complex situations where for instance the objects of interest are not all visible at all times or when stimulus exploration is not homogeneous. The typicical situation is a visual stimulus that is scrolled or that is explored sequentially. We use a recurrence simulation to illustrate how to measure the actual coupling between behavior streams without biases introduced by the complexity of the situation. Our method takes into account underlying random baselines to compute an unbiased version of the coupling

Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package

This paper describes the R package crqa to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialogue, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in crqa would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between 'correlation' and 'co-visitation' when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results' consistency, of crqa R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect comparability between the two libraries on both levels

Co-regulation of movements during infant feeding

The process by which infants move from liquid feeding to caregiver-assisted spoon feeding of semi-solid food is quite a dramatic transition. In previous studies, we observed that in the weeks after the introduction to solid food, mother-infant dyads showed increased co-regulation and synchronization of their respective feeding behaviors (e.g. offering food, accepting/refusing, timing). Learning this new way of feeding and eating requires that infants coordinate their position and movements with the complementary position and movements of the caregiver. The present study augments the category-based analysis of this co-regulation by the analysis of coupling in the dyads based on automatically extracted movement data. Previously collected video data from 10 mother-infant dyads were re-analyzed for the purpose of this study. Movement trajectories of mother’s hand and infant’s face were obtained by applying an automatic movement detection algorithm (TLD, Kalal et al., 2012; for applications to mother-infant interactions see López Pérez et al., 2017). Coordination was assessed by the method of Diagonal Cross Recurrence Profiles (DCRP), which expresses the degree of synchronization at different time lags. Profiles for each dyad from two different occasions --with one visit in the first week of solid feeding and one approximately 4–5 weeks later-- were compared. The results showed that, on average, most synchronization occurred in the first visit at lag 0. In the second visit there was an average delay in synchronization of about 1 s, with leading behavior starting from the infant. This suggests that the coordination was initially closely synchronized and became somewhat looser over time. Possibly, infants have begun to anticipate and guide the feeding movements enacted by the mother. However, our findings underline the idiosyncratic and complex nature of co-regulation of movements during the introduction of solid food. Whereas some dyads showed signs of increased organization, others seemed to disorganize, re-organize, or showed no organization at all. Many (interacting) factors --both individual and contextual-- may be responsible for the observed differences between dyads. Further research is needed to understand why specific synchronization pathways emerge and whether and how these might relate both to later feeding and eating and to the emergent patterns of participation

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

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