Demos

• Fitting go/no-go using the chi-qsuare approach
  • by Jan Willem de Gee (jwdegee@gmail.com)
• Tutorial for analyzing instrumental learning data with the HDDMrl module
  • OUTLINE
  • 1. Background
  • 2. Installing the module
  • 3. How the RLDDM works
  • 4. Structuring data
  • 5. Running basic model
  • 6. Checking results
  • 7. Posterior predictive checks
  • 8. Parameter recovery
  • 9. Separate learning rates for positive and negative prediction errors
  • Posterior predictive check
  • 10. depends_on vs. split_by
  • 11. Probabilistic binary outcomes vs. normally distributed outcomes
  • 12. HDDMrlRegressor
  • 13. Regular RL without RT
  • Posterior predictive check
• Tutorial for analyzing instrumental learning data with the HDDMnnRL module
  • Load the data
  • Initialize the HDDMnnRL model and sample
  • Save the model
  • Check the posterior results
  • Posterior Predictive Checks