Stochastic Surprise Signatures in Human EEG: A Reproducible Benchmark of Prediction-Error Models Using the ERP CORE Oddball Dataset

The brain continuously generates predictions about incoming sensory input and produces characteristic neural responses when those predictions are violated. In EEG oddball paradigms, these prediction-error responses manifest as the mismatch negativity (MMN) and P3b components. However, “surprise” can be formalized in multiple ways, and no prior study has systematically compared these formulations on the same dataset using single-trial methods. Here, we implemented four hierarchical surprise estimators and applied them to the ERP CORE dataset (N = 39 subjects, auditory MMN and visual P3 paradigms). Using linear mixed-effects encoding models, we found that Bayesian surprise, quantifying the magnitude of belief revision, showed the strongest association with single-trial MMN amplitude among all models tested (uncorrected p = 0.015), though this did not survive Holm–Bonferroni correction for multiple comparisons (p_corrected = 0.062). High multicollinearity among Shannon-based and change-point regressors (VIF > 70) limited the interpretability of direct model comparisons. In a cross-subject decoding analysis, contextual surprise features did not improve classification of stimulus class beyond ERP amplitude features alone (ΔAUC = − 0.093). We conclude that Bayesian surprise shows the strongest trend among competing models, but that stationary oddball paradigms may lack sufficient power to definitively distinguish surprise formulations. All data, code, and analysis pipelines are publicly available.