Proceedings of the Annual Meeting of the Cognitive Science Society

Drift diffusion models are used to model evidence accumulation in two-choice forced-tasks. The traditional approach to fitting Ratcliff’s standard drift diffusion model (where the drift and diffusion are constant) usually involves explicit modeling of the first passage time distributions of the upper and lower boundaries or likelihood approximations. We present the very first technique, to the best of our knowledge, that foregoes use of explicit modeling of the first passage time distributions with a random forest regressor. A random forest regression model that takes the first five moments of the response time distribution, and the upper boundary termination proportion, is used to predict the drift and diffusion parameters from response time data. A training set of response time samples of size 2500 from 121 distinct drift-diffusion pairs is used to train the random forest regressor. On a testing set of 10,000 distinct drift-diffusion combinations with response time sample sizes of 40, we find that our model surpasses techniques that make use of some form of analytical modeling of the first passage time distributions of the boundaries for prediction of the diffusion rate, but not the drift rate. We conclude that the application of machine learning to drift-diffusion modeling of empirical data is a topic worth further investigation.