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Unlocking nonlinear dynamics and multistability from intensive longitudinal data: A novel method.

Jingmeng Cui1, Fred Hasselman2, Anna Lichtwarck-Aschoff1

  • 1Faculty of Behavioural and Social Sciences, University of Groningen.

Psychological Methods
|December 14, 2023
PubMed
Summary

A new R package, fitlandr, analyzes complex psychological dynamics from intensive longitudinal data (ILD). It distinguishes true bistability from mere bimodality, offering a robust tool for nonlinear system analysis.

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Area of Science:

  • Psychology
  • Computational Social Science
  • Data Science

Background:

  • Smart devices enable intensive longitudinal data (ILD) collection, crucial for understanding psychological dynamics.
  • Traditional time-series methods struggle with the nonlinear complexity inherent in psychological systems.
  • Limitations include assumptions of linearity and restricted model forms.

Purpose of the Study:

  • Introduce fitlandr, an R package for analyzing complex psychological dynamics from ILD.
  • Integrate nonparametric estimation of drift-diffusion functions and stability landscapes.
  • Address limitations of existing time-series methods in capturing nonlinear system behavior.

Main Methods:

  • Utilize the multivariate kernel estimator (MVKE) for nonparametric drift-diffusion function estimation.
  • Employ Monte-Carlo estimation of steady-state distributions for stability landscape analysis.
  • Implement fitlandr as an R package for practical application.

Main Results:

  • fitlandr successfully recovers bistable dynamics from simulated emotional systems, even with noise.
  • The method prioritizes dynamic information over distributional information for accurate analysis.
  • Applied to empirical ILD, fitlandr identified bistability in one dataset, despite both showing bimodality, highlighting the distinction.

Conclusions:

  • fitlandr effectively distinguishes true bistability from mere data bimodality in ILD.
  • The R package offers a powerful tool for uncovering nonlinear dynamics in psychological systems.
  • Demonstrates the importance of dynamic information for accurate psychological system analysis.