HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python

By Thomas V. Wiecki, Imri Sofer, Michael J Frank. Front. Neuroinform., 2013

982 citations recorded by the source corpus.

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/

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/c5b083a6410645887782b09795b445f1b4c8b7d7

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Citations in the corpus

Ten simple rules for the computational modeling of behavioral data (2019, background): “For example, hierarchical Bayesian approaches make it possible to fit all participants simultaneously, integrating assumptions about their dependence (e.g. one single group, multiple groups, effects of covariates of interest such as age and so on; Lee, 2011; Lee and Wagenmakers, 2014; Wiecki et al.,”
Large-scale analysis of test–retest reliabilities of self-regulation measures (2019, methodology): “The HDDM uses hierarchical Bayesian modeling to allow simultaneous estimation of both group and individual subject parameters.”
Safe models for risky decisions (2017, methodology): “First, Yechiam and Busemeyer (2005) compared combinations of eight different learning rules with three different choice rules, resulting in 24 different models. All 24 models used an extended version of the utility function of the EV model in which the weight for wins and losses are estimated separa”
Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package (2016, methodology): “In fact, several software packages for this purpose do exist, but most of them focus on only a single class of modeling, such as sequential-sampling models (Matzke et al., 2013; Singmann et al., 2016; Vincent, 2016; Wabersich & Vandekerckhove, 2014; Wiecki, Sofer, & Frank, 2013).”
Oscillatory signatures of monitoring and anticipatory strategies for probabilistic vs deterministic cues (2025, unknown): “To assess the impact of probabilistic priors on decision bias, we estimated the Drift Diffusion Model parameters using the HDDM toolbox ( Wiecki et al., 2013).”