HDDM is a python module that implements Hierarchical Bayesian estimation of Drift Diffusion Models.
Project description
- Contact:
- thomas_wiecki@brown.edu, imri_sofer@brown.edu, michael_frank@brown.edu
- Web site:
- Github:
- Mailing list:
- Copyright:
- This document has been placed in the public domain.
- License:
HDDM is released under the BSD 2 license.
- Version:
- 0.5.5
Purpose
HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.
Features
Uses hierarchical Bayesian estimation (via PyMC) of DDM parameters to allow simultaneous estimation of subject and group parameters, where individual subjects are assumed to be drawn from a group distribution. HDDM should thus produce better estimates when less RT values are measured compared to other methods using maximum likelihood for individual subjects (i.e. DMAT or fast-dm).
Heavily optimized likelihood functions for speed (Navarro & Fuss, 2009).
Flexible creation of complex models tailored to specific hypotheses (e.g. estimation of separate drift-rates for different task conditions; or predicted changes in model parameters as a function of other indicators like brain activity).
Estimate trial-by-trial correlations between a brain measure (e.g. fMRI BOLD) and a diffusion model parameter using the HDDMRegression model.
Built-in Bayesian hypothesis testing and several convergence and goodness-of-fit diagnostics.
Quick-start
The following is a minimal python script to load data, run a model and examine its parameters and fit.
import hddm
# Load data from csv file into a NumPy structured array
data = hddm.load_csv('simple_difficulty.csv')
# Create a HDDM model multi object
model = hddm.HDDM(data, depends_on={'v':'difficulty'})
# Create model and start MCMC sampling
model.sample(2000, burn=20)
# Print fitted parameters and other model statistics
model.print_stats()
# Plot posterior distributions and theoretical RT distributions
model.plot_posteriors()
model.plot_posterior_predictive()
For more information about the software and theories behind it, please see the main publication.
Installation
The easiest way to install HDDM is through Anaconda (available for Windows, Linux and OSX):
Download and install Anaconda.
In a shell (Windows: Go to Start->Programs->Anaconda->Anaconda command prompt) type:
conda install -c pymc pymc conda install -c twiecki hddm
If you want to use pip instead of conda, type:
pip install pandas pip install pymc pip install kabuki pip install hddm
This might require super-user rights via sudo. Note that this installation method is discouraged as it leads to all kinds of problems on various platforms.
If you are having installation problems please contact the mailing list.
How to cite
If HDDM was used in your research, please cite the publication:
Wiecki TV, Sofer I and Frank MJ (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Front. Neuroinform. 7:14. doi: 10.3389/fninf.2013.00014
Published papers using HDDM
Cavanagh, J. F., Wiecki, T. V, Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S. J., & Frank, M. J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14(11), 1462–7. doi:10.1038/nn.2925
Jahfari, S., Ridderinkhof, K. R., & Scholte, H. S. (2013). Spatial frequency information modulates response inhibition and decision-making processes. PloS One, 8(10), e76467. doi:10.1371/journal.pone.0076467
Zhang, J., & Rowe, J. B. (2014). Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model. Frontiers in Neuroscience, 8, 69. doi:10.3389/fnins.2014.00069
Cavanagh, J. F., Wiecki, T. V, Kochar, A., & Frank, M. J. (2014). Eye Tracking and Pupillometry Are Indicators of Dissociable Latent Decision Processes. Journal of Experimental Psychology. General. doi:10.1037/a0035813
Dunovan, K. E., Tremel, J. J., & Wheeler, M. E. (2014). Prior probability and feature predictability interactively bias perceptual decisions. Neuropsychologia. doi:10.1016/j.neuropsychologia.2014.06.024
Michmizos, K. P., & Krebs, H. I. (2014). Reaction time in ankle movements: a diffusion model analysis. Experimental Brain Research. doi:10.1007/s00221-014-4032-8
Getting started
Check out the tutorial on how to get started. Further information can be found in howto and the documentation.
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