Neural microstates underlying categorical speech perception using Bayesian nonparametrics

Abstract

Categorical perception (CP) reflects the human auditory systems ability to map continuous acoustic signals onto discrete categories. Understanding the relationship between neural dynamics and perceptual decisions is central to speech-language processing. Here, we implemented a data-driven approach using Bayesian nonparametrics and machine learning to characterize the relationship between auditory cortical responses and speech categorization behaviors. By applying a hierarchical Dirichlet process hidden Markov Model (HDP-HMM) to source-reconstructed event-related potential (ERP) data, we identified temporally distinct neural microstates that capture the evolving stages of speech categorization without imposing predefined temporal windows. Machine learning classifiers, including extreme gradient boosting (XGBoost), support vector machines, and random forests, were applied to decode prototypical (Tk1/5) versus ambiguous (Tk3) speech sound tokens from the microstate data. Using whole-brain activity, the XGBoost classifier achieved the highest decoding accuracy of 94.1% with an area under the curve (AUC) 94.1% within a specific encoding microstate occurring approximately 200-250 ms after stimulus onset. A reduced set of 15 informative brain regions identified via Shapley additive explanations (SHAP) yielded comparable classification performance (90.3% accuracy; AUC 90.0%), with many regions localized to frontal, temporal, and parietal regions in the left hemisphere. Furthermore, neural activity within these regions robustly predicted listeners behavioral identification slopes (R2 = 0.92, p < 0.00001), linking microstate-specific cortical dynamics to individual differences in perceptual gradiency of speech perception. These findings demonstrate that speech categorization emerges within temporally discrete neural microstates during early sensory-perceptual encoding and is supported by a selective, distributed cortical network with clear behavioral relevance.