An Introduction to Drift Diffusion Modeling for Social Psychologists

Last update: May, 2019

This set of tutorials has been designed to introduce you to using the drift diffusion model (DDM) to examine choices in social and non-social contexts. It assumes that you have a working familiarity with programming. If you do not yet feel comfortable with programming, you may find this introduction to programming for psychologists in Matlab helpful. All code in this tutorial is currently written for R and Matlab. Examples from Python may be added at a later date.

Below, you will find the basic contents and details of this tutorial, including step-by-step instructions for becoming familiar with the DDM, as well as worked examples from papers published in our lab.

The basic drift diffusion model >>

This module consists of a set of examples designed to introduce you to the three basic parameters of the drift diffusion model. It consists of some "test yourself" exercises, complete with code, to assess your understanding and comprehension of the DDM. In addition, this page provides helpful references about the basic DDM. Click here for instructions.

The extended drift diffusion model >>

This module consists of a set of examples designed to introduce you to additional parameters of the DDM that have often been explored in the literature. In addition, this page provides additional helpful references if you would like to conduct a deeper dive into some of the literature on drift-diffusion modeling. Click here for instructions.

Fitting the DDM >>

Once you have a good grasp of the ins and outs of the DDM, you will want to use it to make inferences about specific parameters of the model, which can inform theories about the phenomenon you are studying. This module introduces you to some of the key methods for performing model-fitting and inference. Click here for details.

Simulations and parameter recovery >>

Before you begin using model fitting to explore your own data, it is generally a good idea to conduct simulation and parameter recovery exercises to verify how well your model might do with a particular experimental design and particular expectations about model parameters. Click here to begin a tutorial on this important topic.

A worked example >>

Having gained some familiarity with DDMs in the previous two modules, this module will walk you through a single example of how the DDM can begin to shed light on dual-process vs. single-process models of choice. This example is based on work by Harris, Clithero, & Hutcherson (2018). Click here to begin.

More to come!

This tutorial may be updated in the future to apply more broadly in other programming languages (i.e., Python) and to introduce the interested social psychology student to cutting-edge examples as appropriate. Please check back to see if there are any recent updates.

Thanks for reading, and hope these materials are helpful to you!
Cendri Hutcherson