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
What does this toolbox do?
This toolbox aims to be a complete solution for the analysis of experimental data from discounting tasks.
- Bayesian estimates of discounting parameters, complete with credible intervals.
- Parameters exported to a
.csvfile 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.
Introductory video: https://www.youtube.com/watch?v=kDafp-xB7js
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.