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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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| In: |
Behavior research methods
Year: 2024, Jahrgang: 56, Heft: 4, Pages: 3102–3116 |
| ISSN: | 1554-3528 |
| DOI: | 10.3758/s13428-023-02179-1 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.3758/s13428-023-02179-1
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| Verfasserangaben: | Franziska Henrich, Raphael Hartmann, Valentin Pratz, Andreas Voss, Karl Christoph Klauer |
| Zusammenfassung: | 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. |
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| Beschreibung: | Online veröffentlicht: 28. August 2023 Gesehen am 16.10.2023 |
| Beschreibung: | Online Resource |
| ISSN: | 1554-3528 |
| DOI: | 10.3758/s13428-023-02179-1 |