The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the non-linear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a “Programming Basics” module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.

Objective: In social interactions, humans tend to naturally synchronize their body movements. We investigated interpersonal synchronization in conversations and examined its relationship with personality differences and post‐interaction appraisals.Method: In a 15‐minute semi‐structured conversation, 56 previously‐unfamiliar dyads introduced themselves, followed by self‐disclosing and argumentative conversations. Their bodily movements were video‐recorded in a standardized room (112 young adults, aged 18–33, mean = 20.54, SD = 2.74; 58% Dutch, 31% German, 11% other). Interpersonal bodily synchronization was estimated as (a) synchronization strength using Windowed Lagged Cross‐Correlations and (b) Dynamic Organization (Determinism/Entropy/Laminarity/Mean Line) using Cross‐Recurrence Quantification Analysis. Bodily synchronization was associated with differences in Agreeableness and Extraversion (IPIP‐NEO‐120) and post‐conversational appraisals (affect/closeness/enjoyment) in mixed‐effect models.Results: Agreeable participants exhibited higher complexity in bodily synchronization dynamics (higher Entropy) than disagreeable individuals, who also reported more negative affect afterward. Interpersonal synchronization was stronger among extroverts than among introverts and extroverts appraised conversations as more positive and enjoyable. Bodily synchronization strength and dynamic organization were related to the type of conversation (self‐disclosing/argumentative).Conclusions: Interpersonal dynamics were intimately connected to differences in Agreeableness and Extraversion, varied across situations, and these parameters affected how pleasant, close, and enjoyable each conversation felt.

Synchronizing behaviors in interactions, such as during turn-taking, are often impaired in children with Autism Spectrum Disorder. Therapies that focus on turn-taking generally lead to increased social skills, less interruptions, and silent pauses, however a positive non-demanding environment is therefore thought to be beneficial. Such an environment can be achieved by incorporating animals into therapy. Our study was guided by the following research questions: (1) How can we characterize the interaction between child and therapist during dolphin-assisted therapy, with regard to synchrony in verbalizations (turn-taking) and (2) does synchrony change over the course of six sessions of therapy? To answer these questions, we performed a cross-recurrence quantification analysis on behavioral data of five children, to give a detailed view of the interaction between therapist and child in the context of dolphin-assisted therapy. We were able to detect synchrony (i.e., adequate turn-taking) in all dyads, although not all children improved equally. The differences might be explained by a delayed reaction time of some children, and their level of language development.