Abstract
Many experiments in psychology yield both reaction time and accuracy data. However, no off-the-shelf methods yet exist for the statistical analysis of such data. One particularly successful model has been the diffusion process, but using it is difficult in practice because of numerical, statistical, and software problems. We present a general method for performing diffusion model analyses on experimental data. By implementing design matrices, a wide range of across-condition restrictions can be imposed on model parameters, in a flexible way. It becomes possible to fit models with parameters regressed onto predictors. Moreover, data analytical tools are discussed that can be used to handle various types of outliers and contaminants. We briefly present an easy-touse software tool that helps perform diffusion model analyses.
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This research was supported by Grants GOA/00/02-ZKA4511, GOA/2005/04-ZKB3312, and IUAP P5/24. J.V. and F.T. thank Jeff Rouder for providing the data used in the second application example, and Eric-Jan Wagenmakers and Andrew Heathcote for helpful comments on earlier drafts of this article.
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Vandekerckhove, J., Tuerlinckx, F. Fitting the ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review 14, 1011–1026 (2007). https://doi.org/10.3758/BF03193087
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DOI: https://doi.org/10.3758/BF03193087