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This study introduces a maximum likelihood approach for analyzing dynamical cognitive models using time-ordered data. This method enhances parameter estimation and model comparison for predicting behaviors like saccade generation.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Dynamical models are crucial for understanding cognition.
  • Accurate parameter estimation and model comparison are essential for these models.
  • Existing methods may not fully leverage time-ordered experimental data.

Purpose of the Study:

  • To propose a maximum likelihood approach for analyzing dynamical cognitive models.
  • To apply this method to a dynamical model of saccade generation.
  • To demonstrate its utility in parameter estimation, Bayesian inference, and model comparison.

Main Methods:

  • Developed a maximum likelihood framework for dynamical models with time-ordered data.
  • Employed numerical simulation to compute the likelihood function.
  • Utilized Bayesian inference and hierarchical models for parameter estimation.
  • Applied the approach to a saccade generation model.

Main Results:

  • The likelihood approach enabled efficient parameter estimation and Bayesian inference.
  • Hierarchical models allowed for individual observer inference.
  • The dynamical framework outperformed non-dynamical statistical models.
  • The likelihood evaluation distinguished between model variants with similar predictions on traditional statistics.

Conclusions:

  • The maximum likelihood approach is a powerful framework for dynamical cognitive models.
  • It offers improved parameter estimation, model comparison, and analysis of complex cognitive processes.
  • This method advances the integration of theoretical models with experimental data in psychology.