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Parameterization of connectionist models.

Rafal Bogacz1, Jonathan D Cohen

  • 1Princeton University, Princeton, New Jersey, USA. r.bogacz@bristo.ac.uk

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|January 12, 2005
PubMed
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This study introduces a novel method for optimizing connectionist models by minimizing a cost function, ensuring model outputs closely match empirical data for better cognitive modeling. The approach aids in automatic or manual parameter searches, enhancing model accuracy.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Machine Learning

Background:

  • Connectionist models are widely used to simulate human cognition.
  • Estimating model parameters to accurately reflect empirical data is crucial for model validation.
  • Existing methods may lack efficiency or automation in parameter estimation.

Purpose of the Study:

  • To present a novel method for estimating parameters of connectionist models.
  • To ensure model outputs closely align with empirical data.
  • To provide a tool that aids in both automatic and manual parameter optimization.

Main Methods:

  • Minimization of a cost function comparing model output statistics to subject performance statistics.
  • Utilizing an optimization algorithm to identify parameters that minimize the cost function.

Related Experiment Videos

  • The cost function also assesses the statistical significance of differences between model and data.

    • Main Results:

    • The proposed method effectively estimates connectionist model parameters.
    • The cost function quantifies the discrepancy between model and empirical data statistics.
    • The method facilitates automatic parameter optimization in some cases and assists manual searches in others.

    Conclusions:

    • The developed method offers a robust approach for fitting connectionist models to empirical data.
    • This tool enhances the accuracy and interpretability of computational cognitive models.
    • The method is implemented in Matlab, documented, and freely available.