Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
Fig 14
Using HBI for making inference on Parkinson’s patients data.
A) HBI has been applied to a dataset of 31 PD patients performing a probabilistic reward and punishment learning task. The model space consisted of a null non-learning (NL) model, RL, and the dual-α RL. Protected exceedance probabilities (PXP), model frequencies and estimated parameters of the winning model (the dual-α RL) are plotted. The HBI revealed that the dual-α RL is more likely across PD patients. B) The same model space was fitted to a dataset of 20 healthy control subjects performing the same task. In contrast to PD patients, the RL model is more likely across the control group. In addition to the decision noise, β, and learning rate parameters, both RL models also modeled tendency to repeat or avoid the previous choice regardless of outcomes using a perseveration parameter, p. A permutation test revealed that the dual-α model is more likely than the RL model in PD compared with the controls. The error-bars are obtained by applying the corresponding transformation function on the hierarchical errors and, therefore, are not necessarily symmetric.