Behavior Research Methods

, Volume 39, Issue 4, pp 767–775 | Cite as

Fast-dm: A free program for efficient diffusion model analysis

  • Andreas VossEmail author
  • Jochen Voss


In the present article, a flexible and fast computer program, calledfast-dm, for diffusion model data analysis is introduced. Fast-dm is free software that can be downloaded from the authors’ websites. The program allows estimating all parameters of Ratcliff ’s (1978) diffusion model from the empirical response time distributions of any binary classification task. Fast-dm is easy to use: it reads input data from simple text files, while program settings are specified by command0s in a control file. With fast-dm, complex models can be fitted, where some parameters may vary between experimental conditions, while other parameters are constrained to be equal across conditions. Detailed directions for use of fast-dm are presented, as well as results from three short simulation studies exemplifying the utility of fast-dm.


Data File Diffusion Model Cumulative Distribution Function Drift Rate Parameter Estimation Procedure 
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  1. Conover, W. J. (1999).Practical nonparametric statistics (3rd ed.). New York: Wiley.Google Scholar
  2. Feller, W. (1971).An introduction to probability theory and its applications (2nd ed., Vol. 2). New York: Wiley.Google Scholar
  3. Lee, M. D., Fuss, I. G., &Navarro, D. J. (2007). A Bayesian approach to diffusion models of decision-making and response time. In B. Schölkopf, J. C. Platt, & T. Hoffman (Eds.),Advances in neural information processing systems 19 (pp. 809–815). Cambridge, MA: MIT Press.Google Scholar
  4. Morton, K., &Mayers, D. (1994).Numerical solution of partial differential equations. Cambridge: Cambridge University Press.Google Scholar
  5. Nelder, J. A., &Mead, R. (1965). A simplex method for function minimization.Computer Journal,7, 308–313.Google Scholar
  6. Press, W. H., Teukolsky, S. A., &Vetterling, W. T. (1992).Numerical recipes in C (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
  7. Ratcliff, R. (1978). A theory of memory retrieval.Psychological Review,85, 59–108.CrossRefGoogle Scholar
  8. Ratcliff, R., &Rouder, J. N. (1998). Modeling response times for two-choice decisions.Psychological Science,9, 347–356.CrossRefGoogle Scholar
  9. Ratcliff, R., &Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaching to dealing with contaminant reaction and parameter variability.Psychonomic Bulletin & Review,9, 438–481.CrossRefGoogle Scholar
  10. Vandekerckhove, J., & Tuerlinckx, F. (2006).Fitting the Ratcliff diffusion model to experimental data. Manuscript submitted for publication.Google Scholar
  11. Voss, A., Rothermund, K., &Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation.Memory & Cognition,32, 1206–1220.CrossRefGoogle Scholar
  12. Voss, A., & Voss, J. (in press). A fast numerical algorithm for the estimation of diffusion-model parameters.Journal of Mathematical Psychology.Google Scholar
  13. Wagenmakers, E.-J., van der Maas, H. L. J., &Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy.Psychonomic Bulletin & Review,14, 3–22.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2007

Authors and Affiliations

  1. 1.Institut für PsychologieUniversität FreiburgFreiburgGermany
  2. 2.University of WarwickCoventryEngland

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