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The seven-parameter diffusion model: an implementation in stan for bayesian analyses

Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift r...

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Bibliographic Details
Main Authors: Henrich, Franziska (Author) , Hartmann, Raphael (Author) , Pratz, Valentin (Author) , Voß, Andreas (Author) , Klauer, Karl Christoph (Author)
Format: Article (Journal)
Language:English
Published: 2024
In: Behavior research methods
Year: 2024, Volume: 56, Issue: 4, Pages: 3102–3116
ISSN:1554-3528
DOI:10.3758/s13428-023-02179-1
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.3758/s13428-023-02179-1
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Author Notes:Franziska Henrich, Raphael Hartmann, Valentin Pratz, Andreas Voss, Karl Christoph Klauer
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Summary:Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift rate, non-decision time, and relative starting point, in the probabilistic programming language Stan. Stan is a free, open-source software that gives the user much flexibility in defining model properties such as the choice of priors and the model structure in a Bayesian framework. We explain the implementation of the new function and how it is used in Stan. We then evaluate its performance in a simulation study that addresses both parameter recovery and simulation-based calibration. The recovery study shows generally good recovery of the model parameters in line with previous findings. The simulation-based calibration study validates the Bayesian algorithm as implemented in Stan.
Item Description:Online veröffentlicht: 28. August 2023
Gesehen am 16.10.2023
Physical Description:Online Resource
ISSN:1554-3528
DOI:10.3758/s13428-023-02179-1

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