A toolbox for data analysis using the attentional drift-diffusion model.
This toolbox can be used to perform model fitting and to generate simulations for the attentional drift-diffusion model (aDDM), as well as for the classic version of the drift-diffusion model (DDM) without an attentional component.
This toolbox requires the following libraries: * deap * matplotlib * numpy * pandas * scipy
$ pip install addm_toolbox
To make sure everything is working correctly after installation, try:
This should take a while to finish, so maybe go get a cup of tea :)
To get a feel for how the algorithm works, try:
$ addm_demo --display-figures
You can see all the arguments available for the demo using:
$ addm_demo --help
You can also have a look at the code in the following modules: * addm.py contains the aDDM implementation, with functions to generate model simulations and obtain the likelihood for a given data trial. * ddm.py is equivalent to addm.py but for the DDM. * addm_pta_test.py generates an artificial data set for a given set of aDDM parameters and attempts to recover these parameters through maximum a posteriori estimation. * ddm_pta_test.py is equivalent to addm_pta_test.py but for the DDM. * addm_pta_mle.py fits the aDDM to a data set by performing maximum likelihood estimation. * addm_pta_map.py performs model comparison for the aDDM by obtaining a posterior distribution over a set of models. * simulate_addm_true_distributions.py generates aDDM simulations using empirical data for the fixations.
This project is licensed under the GNU GENERAL PUBLIC LICENSE - see the COPYING file for details.
This toolbox was developed as part of a research project in the Rangel Neuroeconomics Lab at the California Institute of Technology.
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