Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
Fig 3
Performance of the HBI in a synthetic dataset including models with the different number of parameters.
10 and 30 artificial subjects were generated according to the RL and dual-α RL models, respectively. A) Model selection by HBI using protected exceedance probabilities (PXP); B) Model frequencies estimated by the HBI. C) Model attribution at the individual level by the HBI. Responsibility estimates are plotted for true attributions (TA) and for false attributions (FA). The HBI shows lower levels of responsibility for FA. Inset: percentage of correct assignment of the model by the HBI at the individual level. D) Model selection performance of NHI, HPE, and HBI; E, F) Error in estimating individual parameters of the RL (E) and the dual-α RL model (F). The estimation error is defined as the absolute difference between estimated parameters and the true parameters. In all plots, error-bars are standard errors of the mean obtained across 20 simulations.