Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series

Tian Linger Xu*, Kaya de Barbaro, Drew H. Abney, Ralf F. A. Cox

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
68 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number1457
Number of pages19
JournalFrontiers in Psychology
Volume11
DOIs
Publication statusPublished - 24-Jul-2020

Keywords

  • time series analysis
  • data visualization
  • burstiness
  • cross recurrence quantification analysis
  • Granger causality
  • high-density behavior data
  • CROSS RECURRENCE QUANTIFICATION
  • GRANGER-CAUSALITY
  • INTERACTIVE ALIGNMENT
  • DYNAMICS
  • INFANTS
  • PATTERNS
  • EMBODIMENT
  • COMPLEXITY
  • ATTENTION
  • MODELS

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