Weighting of neural prediction error by rhythmic complexity: a predictive coding account using Mismatch Negativity

Abstract

The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct interval durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of 5 tones (four interonset intervals) and of a given level of entropy: zero (isochronous), medium‐entropy (two distinct interval durations), or high‐entropy (four distinct interval durations). Occasionally, the fourth tone was shifted forward in time by either 100 ms (small deviation), or 300 ms (large deviation). According to the predictive coding framework, high‐entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding a smaller prediction error for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine‐grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.

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