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Testing and improving the robustness of amortized bayesian inference for cognitive models.

Yufei Wu1, Stefan T Radev2, Francis Tuerlinckx1

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Summary

Robust parameter estimation in cognitive models is improved using amortized Bayesian inference. Data augmentation with a Cauchy distribution enhances estimator robustness against contaminants, making it practical for outlier challenges.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Parameter estimation in cognitive models is susceptible to contaminant observations.
  • Robustness is crucial for reliable model parameter estimation, especially with noisy data.

Purpose of the Study:

  • To improve the robustness of parameter estimation in cognitive models using amortized Bayesian inference.
  • To develop and evaluate a data augmentation method for training robust estimators.

Main Methods:

  • Systematic analyses were conducted on a toy normal distribution example and the drift diffusion model.
  • A data augmentation approach incorporating a contamination distribution (Cauchy) during training was proposed.
  • Robust estimators were evaluated for accuracy and efficiency loss compared to a standard estimator.

Main Results:

  • Introducing Cauchy contaminants during training significantly enhanced the robustness of the neural density estimator.
  • Robust estimators demonstrated bounded sensitivity functions and a substantially higher breakdown point.
  • The proposed method showed practical implementation with minimal accuracy and efficiency loss.

Conclusions:

  • Amortized Bayesian inference combined with data augmentation offers a practical solution for robust parameter estimation.
  • The method is broadly applicable in fields facing challenges with outlier detection and removal.
  • This approach enhances the reliability of cognitive model parameter estimation in the presence of contaminants.