Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
Fig 8
Performance of the HBI in a large model space.
HBI was tested in a large model space including RL, dual-α (DA) RL, Kalman filter (KF) and actor-critic (AC) models in four scenarios. In each scenario, one model (the dominant model) was used to generate 30 subjects. Other models were used to generate 10 subjects. A) Model selection by HBI using protected exceedance probabilities (PXP). B) Model frequencies estimated by the HBI. Note that in each scenario, the model frequency of the dominant model is 0.5 and it is about 0.17 for the other models. C) Model selection performance (at 50%) of NHI, HPE, and HBI. D) Error in estimating individual parameters across both models and parameters. Estimation errors are computed on the normally distributed parameters, 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.