Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

Frontiers in Psychology

article

Tian Linger Xu¹  Kaya de Barbaro²  Drew H. Abney³  Ralf F. A. Cox⁴ 

[1] Department of Psychological and Brain Sciences, Indiana University, United States;Department of Psychology, The University of Texas at Austin, United States;Department of Psychology, Center for Cognition, University of Cincinnati, United States;Department of Psychology, University of Groningen

关键词: time series analysis;    data visualization;    burstiness;    cross recurrence quantification analysis;    Granger causality;    high-density behavior data;   

DOI  :  10.3389/fpsyg.2020.01457

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY