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Diffusion models for simple two-choice decision-making have achieved prominence in psychology and neuroscience. The standard model views decision-making as a process in which noisy evidence is accumulated until one of the two response criteria is reached, at which point the associated response is made. The criteria represent the amount of evidence needed to make a decision and they reflect the decision maker's response biases and speed-accuracy trade-off settings. In this article, we review the regularities in experimental data that a model must explain. These include the relation between accuracy and mean response times, the shapes of response time distributions for correct and error responses and how they change with experimental variables, and individual differences in response time and accuracy. These relations are sometimes overlooked by researchers, but, taken together, they provide extremely strong tests of models.

Modeling Regularities in Response Time and Accuracy Data With the Diffusion Model