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An EZ Bayesian hierarchical drift diffusion model for response time and accuracy.

Adriana F Chávez De la Peña1,2, Joachim Vandekerckhove3,4

  • 1Department of Cognitive Sciences, University of California, Irvine, CA, USA.

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Summary
This summary is machine-generated.

The EZ-diffusion model simplifies choice response time analysis, enabling direct parameter calculation from data. This probabilistic formulation offers a hyper-efficient proxy for the popular drift diffusion model in cognitive psychometrics.

Keywords:
Cognitive psychometricsEZ diffusionHierarchical BayesianIndirect inference

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychometrics

Background:

  • The drift diffusion model is widely used for analyzing choice response times.
  • Calculating diffusion model parameters typically requires computationally intensive methods.
  • Existing methods present challenges for direct parameter estimation from data.

Purpose of the Study:

  • Introduce a probabilistic formulation of the EZ-diffusion model.
  • Provide a hyper-efficient proxy for the drift diffusion model.
  • Facilitate direct calculation of diffusion model parameters from empirical data.

Main Methods:

  • Developed a probabilistic formulation based on sampling distributions of summary statistics.
  • Utilized normal and binomial distributions for the model.
  • Inverted equations linking diffusion model parameters to summary statistics (accuracy, mean/variance of response times).

Main Results:

  • Demonstrated the validity of the proxy model through extensive simulations.
  • Showed that regression parameter recovery is good, despite some bias in individual parameter recovery.
  • Highlighted the utility of the method for cognitive psychometrics and explanatory cognitive modeling.

Conclusions:

  • The probabilistic EZ-diffusion model serves as a computationally efficient proxy for the drift diffusion model.
  • Its implementation in probabilistic programming languages and JASP facilitates broader application.
  • Casting EZ-diffusion within Bayesian generative models enables advanced analyses and extensions.