Skip to content

Latest commit

 

History

History
37 lines (25 loc) · 2.41 KB

README.md

File metadata and controls

37 lines (25 loc) · 2.41 KB
Raw

Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks

Vincent, B. T. (2016) Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks, Behavior Research Methods. 48(4), 1608-1620. doi:10.3758/s13428-015-0672-2

Issue Stats

What does this toolbox do?

This toolbox aims to be a complete solution for the analysis of experimental data from discounting tasks.

Key features:

  • Bayesian estimates of discounting parameters, complete with credible intervals.
  • Parameters exported to a .csv file for analysis in JASP.
  • Optionally use hierarchical inference to improve participant-level estimates.
  • A variety of models are available:
    • 1-parameter discount functions: exponential, hyperbolic.
    • 2-parameter discount functions: hyperboloid
    • Also, hyperbolic discounting + magnitude effect, where discount rates vary as a function of reward magnitude.
  • Explicit modelling of participant errors provides more robust parameter estimates of discounting parameters.
  • Posterior predictive checks help evaluate model goodness and aid data exclusion decisions.
  • Publication quality figures.

Resources

Documentation: https://drbenvincent.github.io/delay-discounting-analysis/

Introductory video: https://www.youtube.com/watch?v=kDafp-xB7js

Questions, comments

Please use the GitHub Issues feature to ask question, report a bug, or request a feature. You'll need a GitHub account to do this, which isn't very hard to set up.

But you could always email me or tweet me @inferenceLab instead.

I'm very happy if people would like to contribute to the toolbox in any way. Please see the CONTRIBUTING.md document.