Repository for fitting Drift-Diffusion models with identifiable within-trial noise parameters in Python using BayesFlow (and JAGS and Stan)
Authors: Michael D. Nunez from the Psychological Methods group at the University of Amsterdam
Nunez, M. D., Schubert, A.-L., Frischkorn, G. T., & Oberauer, K. (2024). Cognitive models of decision-making with identifiable parameters: Diffusion Decision Models with within-trial noise PsyArXiv. https://doi.org/10.31234/osf.io/h4fde
Python 3 and Scientific Python libraries
See BayesFlow install instructions to create a BayesFlow conda environment for the most stable method to run these scripts. It is also recommended to keep a local version of BayesFlow on your computer because the package is being actively developed. For this project, I used BayesFlow version 1.1 with Python 3.10.
See also yaml/bayesflow.yml.
For JAGS installation steps in Ubuntu, see jags_wiener_ubuntu.md
Python Repository: pyjags, can use pip:
pip install pyjagsSee also yaml/pyjags.yml
For this project I used PyStan 2. The newest version of PyStan was PyStan 3, but I didn't find PyStan 3 as easy to use with custom diagnostic and plotting scripts as PyStan 2.
Here are the docs for PyStan 2
See also yaml/pystan.yml
The repository can be cloned with git clone https://github.com/mdnunez/bayesflow_nddms.git
The repository can also be downloaded via the Code -> Download zip buttons above on this Github page.
bayesflow_nddms is licensed under the GNU General Public License v3.0 and written by Michael D. Nunez from the Psychological Methods group at the University of Amsterdam.
(see also References in preprint of Citation above)
Ghaderi-Kangavari, A., Rad, J.A. & Nunez, M.D. (2023). A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials. Computational Brain & Behavior https://doi.org/10.1007/s42113-023-00167-4
Nunez, M. D., Fernandez, K., Srinivasan, R., & Vandekerckhove, J. (2024). A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02331-x
Mattes, A., Porth, E., & Stahl, J. (2022). Linking neurophysiological processes of action monitoring to post-response speed-accuracy adjustments in a neuro-cognitive diffusion model. NeuroImage, 247, 118798. https://doi.org/10.1016/j.neuroimage.2021.118798