Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations

Udo Boehm, Jeffrey Annis, Michael J. Frank, Guy E. Hawkins, Andrew Heathcote, David Kellen, Angelos Miltiadis Krypotos, Veronika Lerche, Gordon D. Logan, Thomas J. Palmeri, Don van Ravenzwaaij, Mathieu Servant, Henrik Singmann, Jeffrey J. Starns, Andreas Voss, Thomas V. Wiecki, Dora Matzke, Eric Jan Wagenmakers

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM's success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM's across-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM's across-trial variability parameters.

Original languageEnglish (US)
Pages (from-to)46-75
Number of pages30
JournalJournal of Mathematical Psychology
StatePublished - Dec 2018


  • Across-trial variability parameters
  • Diffusion Decision Model
  • Parameter estimation

ASJC Scopus subject areas

  • General Psychology
  • Applied Mathematics


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