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

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  1. Chandrasekaran, Chandramouli; Blurton, Steven P.; Gondan, Matthias: Audiovisual detection at different intensities and delays (2019)
  2. Boehm, Udo; Annis, Jeffrey; Frank, Michael J.; Hawkins, Guy E.; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D.; Palmeri, Thomas J.; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey J.; Voss, Andreas; Wiecki, Thomas V.; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations (2018)
  3. Blurton, Steven P.; Kesselmeier, Miriam; Gondan, Matthias: The first-passage time distribution for the diffusion model with variable drift (2017)
  4. Schubert, Anna-Lena; Hagemann, Dirk; Voss, Andreas; Bergmann, Katharina: Evaluating the model fit of diffusion models with the root mean square error of approximation (2017)
  5. Dylan Molenaar;Francis Tuerlinckx; Han van der Maas: Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT (2015) not zbMATH
  6. Ratcliff, Roger; Thompson, Clarissa A.; McKoon, Gail: Modeling individual differences in response time and accuracy in numeracy (2015) MathEduc
  7. Dominik Wabersich; Joachim Vandekerckhove: The RWiener Package: an R Package Providing Distribution Functions for the Wiener Diffusion Model (2014) not zbMATH
  8. Donkin, Chris; Brown, Scott; Heathcote, Andrew: Drawing conclusions from choice response time models: a tutorial using the linear ballistic accumulator (2011)
  9. White, Corey N.; Ratcliff, Roger; Vasey, Michael W.; McKoon, Gail: Using diffusion models to understand clinical disorders (2010)
  10. Voss, Andreas; Voss, Jochen: A fast numerical algorithm for the estimation of diffusion model parameters (2008)