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

Abstract

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-β) 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.

The Structure and Statistics of Language Jointly Shape Cross-frequency Neural Dynamics During Spoken Language Comprehension

“…Given the synchronous increase in coupling for syntactic features (depth and close) we suggest that this internal model update is aligned with the slower rhythm of (predicted) phrasal boundaries [73]. We find these results to align with the idea of multiplexed and alternating predictive and integrative mechanisms between, respectively, top-down and bottom-up information as seen in Fontolan et al [114] or in the syllabic-level speech inference model from Hovsepyan et al [84].…”

“…novel, information deviating from internally generated predictions, which results in an update of the internal model [73, 74]. Top-down predictions, but also updates, have been already linked to beta power modulation [84]. All in all, this mechanism, if supported via delta-phase, is bound to endogenously generated predictions and thus to the internal model of the listener.…”

A brain-rhythm based computational framework for semantic context and acoustic signal integration in speech processing

“…While BRyBI shows promising results, it leads to multiple avenues for extensions and improvements through the implementation of more biological mechanisms for rhythm generation, the incorporation of phase-amplitude coupling (PAC) mechanisms, and considering the role of beta in the inference hierarchy [98].…”

“…Following the example of previous similar models [23, 73, 98], the GM splits each syllable into 8 parts. It allows more flexibility in shaping the auditory spectrogram of syllables and phonemes.…”

Neural manifolds carry reactivation of phonetic representations during semantic processing

“…The specific mechanisms underlying the emergence of such phonemic-to-semantic interface remain to be uncovered. For example, distinct phonetic features could be represented either sequentially (Hickok and Poeppel, 2007; Dehaene et al, 2015) or persistently (Perdikis et al, 2011; Yi et al, 2019; Martin, 2020) with more resolved temporal mechanisms (Fontolan et al, 2014; Hovsepyan et al, 2023) before being pooled together in a semantic representation.…”