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Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series.

Tian Linger Xu1, Kaya de Barbaro2, Drew H Abney3

  • 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.

Frontiers in Psychology
|August 15, 2020
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Summary
This summary is machine-generated.

This study introduces four practical methods for analyzing complex behavioral time series data. These techniques help researchers uncover temporal structures in high-density multimodal behavior, advancing psychological research.

Keywords:
Granger causalityburstinesscross recurrence quantification analysisdata visualizationhigh-density behavior datatime series analysis

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Area of Science:

  • Behavioral Science
  • Computational Psychology
  • Data Science

Background:

  • Massive multimodal behavioral datasets are increasingly available.
  • Analyzing temporal structures in behavior presents significant challenges for psychologists.
  • Existing methods are often insufficient for high-density time series data.

Purpose of the Study:

  • To introduce four accessible techniques for analyzing high-density multimodal behavior data.
  • To provide practical tools and training for quantifying temporal patterns in behavioral time series.
  • To enable researchers to discover dynamic organizational principles in behavior.

Main Methods:

  • Time series visualization
  • Burstiness calculation for temporal event distribution
  • Cross-Recurrence Quantification Analysis (CRQA) for non-linear dynamics
  • Granger Causality for directional relationships

Main Results:

  • The paper presents four distinct analytical techniques.
  • Each technique is accompanied by conceptual background, empirical data examples, and ready-to-use Matlab scripts.
  • A "Programming Basics" module is included for beginner programmers.

Conclusions:

  • These materials offer a practical introduction to analyzing temporal structures in high-density behavioral data.
  • The provided methods and scripts empower psychologists to explore complex behavioral dynamics.
  • This work facilitates the discovery of behavioral organization, origins, and development.