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 nonlinear 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. (shrink)

Gestures and speech are clearly synchronized in many ways. However, previous studies have shown that the semantic similarity between gestures and speech breaks down as people approach transitions in understanding. Explanations for these gesture–speech mismatches, which focus on gestures and speech expressing different cognitive strategies, have been criticized for disregarding gestures’ and speech's integration and synchronization. In the current study, we applied three different perspectives to investigate gesture–speech synchronization in an easy and a difficult task: temporal alignment, semantic similarity, and (...) complexity matching. Participants engaged in a simple cognitive task and were assigned to either an easy or a difficult condition. We automatically measured pointing gestures, and we coded participant's speech, to determine the temporal alignment and semantic similarity between gestures and speech. Multifractal detrended fluctuation analysis was used to determine the extent of complexity matching between gestures and speech. We found that task difficulty indeed influenced gesture–speech synchronization in all three domains. We thereby extended the phenomenon of gesture–speech mismatches to difficult tasks in general. Furthermore, we investigated how temporal alignment, semantic similarity, and complexity matching were related in each condition, and how they predicted participants’ task performance. Our study illustrates how combining multiple perspectives, originating from different research areas (i.e., coordination dynamics, complexity science, cognitive psychology), provides novel understanding about cognitive concepts in general and about gesture–speech synchronization and task difficulty in particular. (shrink)