HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ2-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Kang, Inhan; Ratcliff, Roger; Voskuilen, Chelsea: A note on decomposition of sources of variability in perceptual decision-making (2020)
  2. Murray, Carolyn A.; de Larrea-Mancera, E. Sebastian Lelo; Glicksohn, Arit; Shams, Ladan; Seitz, Aaron R.: Revealing multisensory benefit with diffusion modeling (2020)
  3. Shamloo, Farzin; Hélie, Sébastien: A study of individual differences in categorization with redundancy (2020)
  4. 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)
  5. Blurton, Steven P.; Kesselmeier, Miriam; Gondan, Matthias: The first-passage time distribution for the diffusion model with variable drift (2017)
  6. Gluth, Sebastian; Rieskamp, Jörg: Variability in behavior that cognitive models do not explain can be linked to neuroimaging data (2017)
  7. Hawkins, Guy E.; Mittner, Matthias; Forstmann, Birte U.; Heathcote, Andrew: On the efficiency of neurally-informed cognitive models to identify latent cognitive states (2017)
  8. Miletić, Steven; Turner, Brandon M.; Forstmann, Birte U.; van Maanen, Leendert: Parameter recovery for the leaky competing accumulator model (2017)
  9. Nunez, Michael D.; Vandekerckhove, Joachim; Srinivasan, Ramesh: How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters (2017)
  10. Ratcliff, Roger; Thompson, Clarissa A.; McKoon, Gail: Modeling individual differences in response time and accuracy in numeracy (2015) MathEduc
  11. Dominik Wabersich; Joachim Vandekerckhove: The RWiener Package: an R Package Providing Distribution Functions for the Wiener Diffusion Model (2014) not zbMATH