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Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.

Antti Kangasrääsiö1, Jussi P P Jokinen2, Antti Oulasvirta2

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Summary
This summary is machine-generated.

Accurate parameter fitting for computational cognitive models is crucial. Modern methods like approximate Bayesian computation offer efficient, informative, and reproducible ways to fit complex cognitive models, surpassing traditional techniques.

Keywords:
Approximate Bayesian computationBayesian optimizationCognitive modelsComputational statisticsInferenceMachine learningParameter estimation

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Computational cognitive models offer deep insights but face challenges in parameter fitting.
  • Traditional methods like grid search and Nelder-Mead may be insufficient for complex models.
  • Robust fitting is essential to avoid misinterpreting model validity.

Purpose of the Study:

  • To investigate the efficacy of modern parameter fitting methods for computational cognitive models.
  • To compare Bayesian optimization and approximate Bayesian computation against traditional methods.
  • To highlight the importance of fitting methodology in cognitive science.

Main Methods:

  • Reanalysis of two established computational models: skill acquisition and visual search.
  • Comparison of grid search, Nelder-Mead, Bayesian optimization, and approximate Bayesian computation.
  • Evaluation of methods based on efficiency, informativeness, and parameter uncertainty estimation.

Main Results:

  • Bayesian methods, particularly approximate Bayesian computation, demonstrate superior efficiency and informativeness.
  • Modern Bayesian approaches effectively estimate the uncertainty of fitted parameter values.
  • Contrasting results highlight limitations of traditional fitting techniques for complex models.

Conclusions:

  • Approximate Bayesian computation is an efficient and informative method for fitting computational cognitive models.
  • This approach facilitates reproducible research in computational cognitive science.
  • Adopting advanced fitting methods is vital for the advancement of cognitive modeling.