Rhythmic modulation of prediction errors: A top-down gating role for the beta-range in speech processing

Natural speech perception requires processing the ongoing acoustic input while keeping in mind the preceding one and predicting the next. This complex computational problem could be handled by a dynamic multi-timescale hierarchical inferential process that coordinates the information flow up and down the language network hierarchy. Using a predictive coding computational model (Precoss-beta) that identifies online individual syllables from continuous speech, we address the advantage of a rhythmic modulation of up and down information flows, and whether beta oscillations could be optimal for this. In the model, and consistent with experimental data, theta and low-gamma neural frequency scales ensure syllable-tracking and phoneme-level speech encoding, respectively, while the beta rhythm is associated with inferential processes. We show that a rhythmic alternation of bottom-up and top-down processing regimes improves syllable recognition, and that optimal efficacy is reached when the alternation of bottom-up and top-down regimes, via oscillating prediction error precisions, is in the beta range (around 20-30 Hz). These results not only demonstrate the advantage of a rhythmic alternation of up- and down-going information, but also that the low-beta range is optimal given sensory analysis at theta and low-gamma scales. While specific to speech processing, the notion of alternating bottom-up and top-down processes with frequency multiplexing might generalize to other cognitive architectures. During speech perception, our brain achieves continuous acoustic analysis of the ongoing speech signal, its transformation into linguistic representations, and the prediction of the most likely next words or syllables. In this computational study, we address the biological mechanisms underpinning the coordination of these operations during natural speech processing. Using a model that recognizes on-line syllables in natural sentences, we show that neural activity at specific rhythms is dedicated to specific operations, and that while the theta and low-gamma rhythms are engaged in speech features signaling and encoding, the more endogenous low-beta rhythm drives the rhythmic and coordinated modulation of prediction errors across levels of hierarchy.