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Basics of time series analysis12/10/2023 ![]() The first challenge is that our world is profoundly multimodal ( Smith and Gasser, 2005 Kolodny and Edelman, 2015). Leveraging these massive new datasets to characterize the complex processes of human behavior presents outstanding opportunities, as well as challenges for psychologists in all fields.Īnalysis of these rich corpora of behavioral data faces three main challenges. This has led to the curation of massive multimodal corpora of behavior ( Yang and Hsu, 2010 Franchak and Adolph, 2014 Smith et al., 2015 Matthis et al., 2018). In the past two decades, technological advances in sensing and mobile computing have provided researchers with new ways to collect behavioral data at a fine temporal scale both in and out of the laboratory ( de Barbaro, 2019). By studying behavior as it unfolds over time, we are able to reveal rich source of information about its dynamic organization, origins, and development ( Bakeman and Quera, 2011). The micro-dynamics of infants’ behaviors and their interactions with the world shape their longitudinal trajectories across domains, from motor and language development to socio-emotional development and psychopathology ( Thelen, 2000 Adolph and Berger, 2006 Masten and Cicchetti, 2010 Landa et al., 2013 Blair et al., 2015 West and Iverson, 2017). All forms of behavior are organized as cascades of real-time events ( Spivey and Dale, 2006 Adolph et al., 2018). We believe this is even more true for studying human development. Elman highlighting the importance of characterizing the temporal structure of behavior for understanding human cognition ( Elman, 1990). Our title was inspired by a highly influential paper by Jeffrey L. 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. 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. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. 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. 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. However, along with these new opportunities come new challenges. 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. The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. 4Department of Psychology, University of Groningen, Groningen, Netherlands.3Department of Psychology, Center for Cognition, Action & Perception, University of Cincinnati, Cincinnati, OH, United States.2Department of Psychology, The University of Texas at Austin, Austin, TX, United States. ![]() 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.Tian Linger Xu 1*† Kaya de Barbaro 2† Drew H.
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