Fig 10. Modeling results.

Exceedance probabilities (φ) resulting from the random-effects family-wise model comparison. (A) Dirichlet-Categorical (DC) model, Hidden Markov Model (HMM) and null model family comparison, thresholded at φ > 0.99 and applied for data reduction at all further levels. (B) Family comparison within the winning DC family, thresholded at φ > 0.95: first and second order transition probability models (TP₁, TP₂). (C) Family comparison within the winning DC family, thresholded at φ > 0.95: first order transition probability (TP₁), alternation probability (AP) and stimulus probability (SP) models and applied at the final level. (D) Unthresholded protected exceedance probabilities ($\tilde{\phi }$) resulting from model comparison of surprise models within the winning DC TP1 family: Large discrete topographies show the electrode clusters of predictive surprise (PS) in red, Bayesian surprise (BS) in green and confidence-corrected surprise (CS) in blue. White asterisks indicate $\tilde{\phi }>0.95$ of single electrodes. Small continuous topographies display the converged variational expectation parameter m_β. This parameter may be interpreted as a β weight in regression, indicating the strength and directionality of the weight on the model regressor that maximizes the regressor’s fit to the EEG data (see S2 Appendix).