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.

Dynamical systems approaches to social coordination underscore how participants’ local actions give rise to and maintain global interactive patterns and how, in turn, they are also shaped by them. Developmental research can deliver important insights into both processes: (1) the stabilization of ways of interacting, and (2) the gradual shaping of the agentivity of the individuals. In this article we propose that infants’ agentivity develops out of participation, i.e. acting a part in an interaction system. To investigate this development this article focuses on the ways in which participation in routinized episodes may shape infant’s agentivity in social events. In contrast to existing research addressing more advanced forms of participating in social routines, our goal was to assess infants’ early participation as evidence of infants grasping of their behavior as agentive. In our study, 19 Polish mother–infant dyads were filmed playing peekaboo when the infants were 4 and 6 months of age. We operationalized infants’ participation in the peekaboo in terms of their use of various behaviors across modalities during specific phases of the game: We included smiles, vocalizations, and attempts to cover and uncover themselves or their mothers. We hypothesized that infants and mothers would participate actively in the routine by regulating their behavior so as to adhere to the routine format. Furthermore, we hypothesized that infants who experienced more scaffolding would be able to adopt a more active role in the routine. We operationalized scaffolding by analyzing mothers’ use of specific peekaboo structures that allowed infants to anticipate when it was their turn to act. Results suggested that infants as young as 4 months of age engaged in peekaboo and took up turns in the game, and that their participation increased at 6 months of age. Crucially, our results suggest that infants’ behavior was organized by the global structure of the peekaboo game, because smiles, vocalizations, and attempts to uncover occurred significantly more often during specific phases rather than being evenly distributed across the whole interaction. Furthermore, the way mothers structured the game at 4 months predicted infant participation at both 4 and 6 months of age.

In this article, we note that despite being a multimodal phenomenon, turn-taking has still been investigated mostly as being unimodal. Based on theoretical positions emphasizing that communication is organized jointly by interaction partners, we identify the challenge of assessing human sequential behavior that is (a) spread across different modalities and (b) co-constructed with a partner. By analyzing a corpus of mother-child dyads with Cross Recurrence Quantification Analysis and Frequent Pattern Mining, we offer novel steps toward understanding multimodal turn-taking.