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Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Viet Hung Dao1, David Gunawan2, Minh-Ngoc Tran2

  • 1School of Economics, University of New South Wales.

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This summary is machine-generated.

We developed a new computational method, cross-validation via variational Bayes (CVVB), to efficiently compare complex psychological models. CVVB makes advanced model comparison techniques accessible for hierarchical cognitive models, saving significant computational time.

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychometric Methods

Background:

  • Model comparison is crucial for advancing psychological theory.
  • Hierarchical cognitive models are increasingly used but computationally intensive to compare.
  • Existing methods like marginal likelihood are computationally prohibitive for complex models.

Purpose of the Study:

  • To develop computationally efficient algorithms for Bayesian inference in hierarchical cognitive models.
  • To introduce a novel cross-validation approach using variational Bayes (CVVB) for model comparison.
  • To enable feasible comparison of larger, more complex cognitive models.

Main Methods:

  • Developed novel variational Bayes (VB) algorithms for feasible and efficient inference in hierarchical models.
  • Implemented a VB-based Bayesian prediction tool for cross-validation (CVVB).
  • Applied CVVB to investigate latent components driving the speed-accuracy tradeoff in decision-making.

Main Results:

  • CVVB provides a computationally efficient alternative for model comparison.
  • The CVVB approach demonstrated strong agreement with marginal likelihood comparisons.
  • CVVB successfully identified key processing components in decision-making models.

Conclusions:

  • CVVB makes cross-validation feasible for complex hierarchical cognitive models.
  • This method significantly reduces computational cost for model comparison.
  • The approach expands the scope of theoretically relevant psychological models that can be compared.