Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Abstract

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants in the expected pattern of sensory input. Studies show that MMN diminishes when a sequence pattern becomes more predictable over a longer timescale. This implies that MMN is comprised of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms which peak early in the central-frontal area and late in a more frontal area, respectively. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multi-tiered neural marker, applicable in clinical settings.