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The Seven-parameter Diffusion Model: an Implementation in Stan for Bayesian Analyses.

Franziska Henrich1, Raphael Hartmann2, Valentin Pratz3

  • 1Department of Psychology, University of Freiburg, Engelbergerstraße 41, D-79106, Freiburg, Germany. franziska.henrich@psychologie.uni-freiburg.de.

Behavior Research Methods
|August 28, 2023
PubMed
Summary
This summary is machine-generated.

We implemented a flexible seven-parameter diffusion model in Stan for cognitive process analysis. This Bayesian approach accurately recovers parameters and validates the algorithm for response time data.

Keywords:
Bayesian inferenceModel fittingRatcliff diffusion modelStan function

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychometrics

Background:

  • Diffusion models are crucial for understanding cognitive processes using response and response-time data.
  • Existing models often lack flexibility in capturing inter-trial variability.

Purpose of the Study:

  • To implement a comprehensive seven-parameter diffusion model in the Stan probabilistic programming language.
  • To incorporate inter-trial variability in drift rate, non-decision time, and relative starting point.
  • To provide a flexible Bayesian framework for cognitive modeling.

Main Methods:

  • Implementation of a seven-parameter diffusion model within the Stan environment.
  • Utilizing a Bayesian framework with flexible prior and model structure definitions.
  • Performance evaluation through simulation studies focusing on parameter recovery and calibration.

Main Results:

  • The simulation study demonstrated generally good recovery of diffusion model parameters.
  • Simulation-based calibration confirmed the validity of the Bayesian algorithm implemented in Stan.
  • The implementation offers enhanced flexibility for cognitive modeling research.

Conclusions:

  • The Stan implementation of the seven-parameter diffusion model provides a robust and flexible tool for cognitive science research.
  • This approach validates the use of Bayesian methods in Stan for analyzing complex cognitive data.
  • The model's ability to handle inter-trial variability enhances its utility for detailed cognitive process analysis.