PyDDM Cookbook

Here are a list of examples of common model features and how to implement them in PyDDM. If you created an example or model in PyDDM and would like it to be added to the cookbook, please send it to us so we can add it. Include the author(s) of the example or model and optionally a literature reference so that we can give you proper credit and direct users to your paper!

Download (all models in the cookbook)

Mixture models (Contaminant RTs)

I want to fit a distribution which has contaminant RTs distributed according to:

Task paradigms

Here are some examples of potential task paradigms that can be simulated with PyDDM.

Objective functions

I don’t want to use the default recommended objective function (negative log likelihood) but would rather use:

(Note that changing the objective function to something other than likelihood will not speed up model fitting.)

Fitting methods

I don’t want to fit using the default recommended method (differential evolution), but would rather fit using:

(While using a fitting method other than differential evolution will likely reduce the time needed for fitting models, other methods may not offer robust parameter estimation for high-dimensional models.)