title: The EZ diffusion model provides a powerful test of simple empirical effects.
abstract: Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85, 59-108, 1978), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. (Psychonomic Bulletin & Review, 14, 3-22, 2007) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the data-generating full diffusion model.
authors.affiliation: University of Groningen, Department of Psychology, Grote Kruisstraat 2/1, Heymans Building, room 169, 9712, TS, Groningen, The Netherlands. d.van.ravenzwaaij@rug.nl.
authors.forename: Don
authors.initials: D
authors.lastname: van Ravenzwaaij
authors.affiliation: University of New South Wales, Sydney, Australia.
authors.forename: Chris
authors.initials: C
authors.lastname: Donkin
authors.affiliation: University of California, Irvine, CA, USA.
authors.forename: Joachim
authors.initials: J
authors.lastname: Vandekerckhove
mesh_terms: D002755:Choice Behavior / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D011597:Psychomotor Performance / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*
keywords: Model complexity; Power; Response time analysis; Sequential accumulator models