Localizing hierarchical prediction errors and precisions during an oddball task with volatility: Computational...

The auditory mismatch negativity (MMN) has been widely used to investigate deficits in early auditory information processing, particularly in psychosis. Predictive coding theories suggest that impairments in sensory learning may arise from disturbances in hierarchical message passing, likely due to aberrant precision-weighting of prediction errors (PEs). This study employed a modified auditory oddball paradigm with varying phases of stability and volatility to disentangle the impact of hierarchical PEs on auditory MMN generation in 43 healthy controls (HCs). Single-trial EEG data were modeled with a hierarchical Bayesian model of learning to identify neural correlates of low-level PEs about tones and high-level PEs about environmental volatility. Our analysis revealed a reduced expression of the auditory MMN in volatile compared to stable phases of the paradigm. Additionally, lower Global Functioning (GF): Social scores were associated with a reduced difference waveform at 332 ms after stimulus presentation across the entire MMN paradigm. Further analysis revealed that this association was present during the volatile phase but not the stable phase of the paradigm. Source reconstruction suggested that the association between the stable difference waveform and psychosocial functioning originated in the left superior temporal gyrus. Finally, we found significant EEG signatures of both low- and high-level PEs and precision ratios. Our findings highlight the value of computational models in understanding the neural mechanisms involved in early auditory information processing and their connection to psychosocial functioning.