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This study introduces lagged multidimensional recurrence quantification analysis (RMQRA) to analyze joint dynamics in multivariate time series. The new method reveals leader-follower relationships in group behavioral and physiological data.

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Area of Science:

  • Complex systems analysis
  • Nonlinear dynamics
  • Behavioral and physiological data analysis

Background:

  • Quantifying joint dynamics in multivariate time series is challenging.
  • Existing methods may not fully capture leader-follower interactions within groups.
  • Understanding shared dynamics is crucial in fields like joint action research.

Purpose of the Study:

  • Introduce lagged multidimensional recurrence quantification analysis (RMQRA).
  • Extend RMQRA to quantify joint dynamics and leader-follower relationships in multivariate time series.
  • Apply the method to synthetic and real-world behavioral and physiological data.

Main Methods:

  • Formal presentation of the lagged multidimensional recurrence quantification analysis (RMQRA).
  • Application to synthetic datasets for validation.
  • Analysis of joint action data, including facial expressions and heart rate.
  • Development of a wrapper function for the R 'crqa' package.

Main Results:

  • Demonstrated the capability of RMQRA to quantify joint dynamics in multivariate time series.
  • Successfully identified leader-follower relationships in behavioral and physiological data.
  • Quantified shared dynamics within groups using facial expression and heart rate data.

Conclusions:

  • Lagged multidimensional recurrence quantification analysis (RMQRA) is a powerful tool for investigating complex interactions.
  • The method enhances the understanding of group dynamics and leader-follower relationships.
  • RMQRA provides novel insights into the synchronized behavior of individuals in groups.