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Dogonasheva, O.

Publications and source records attributed to Dogonasheva, O..

2 recordsLinked to original sources

Contextual Prediction Tunes the Tempo of Speech Segmentation

Speech comprehension draws on both temporal structure and contextual prediction, yet how these mechanisms coordinate is poorly understood. Time-compressed speech provides a controlled probe: by degrading temporal structure, it reveals the architecture of ordinary speech comprehension. Using 3x compression with silence insertion, we varied delivery rate, temporal regularity, and boundary alignment (syllabic vs. time-defined) across two behavioural experiments. Comprehension peaked near the upper theta boundary and declined at slower and faster rates. Temporal regularity helped only when boundaries coincided with syllabic onsets, while periodic pacing alone was insufficient. Contextual predictability (word-level entropy) facilitated comprehension when temporal cues were least effective, but only under syllabic segmentation. Computational modeling confirmed that {beta}-mediated contextual prediction selectively benefited syllabic-aligned conditions, was detrimental under time-based segmentation, and better reproduced human pattern overall. Together, these results suggest that contextual prediction is continuously active but behaviorally visible only when temporal scaffolding is insufficient and syllabic structure is preserved.

neuroscience↗

Deciphering the Rhythmic Symphony of Speech: A Neural Framework for Robust and Time-Invariant Speech Comprehension

Unraveling the mysteries of how humans effortlessly grasp speech despite diverse environmental challenges has long intrigued researchers in systems and cognitive neuroscience. This study explores the neural intricacies underpinning robust speech comprehension, giving computational mechanistic proof for the hypothesis proposing a pivotal role for rhythmic, predictive top-down contextualization facilitated by the delta rhythm in achieving time-invariant speech processing. Our Brain-Rhythm-based Inference model, BRyBI, integrates three key rhythmic processes - theta-gamma interactions for parsing phoneme sequences, dynamic delta rhythm for inferred prosodic-phrase context, and resilient speech representations. Demonstrating mechanistic proof-of-principle, BRyBI replicates human behavioral experiments, showcasing its ability to handle pitch variations, time-warped speech, interruptions, and silences in non-comprehensible contexts. Intriguingly, the model aligns with human experiments, revealing optimal silence time scales in the theta- and delta-frequency ranges. Comparative analysis with deep neural network language models highlights distinctive performance patterns, emphasizing the unique capabilities of a rhythmic framework. In essence, our study sheds light on the neural underpinnings of speech processing, emphasizing the role of rhythmic brain mechanisms in structured temporal signal processing - an insight that challenges prevailing artificial intelligence paradigms and hints at potential advancements in compact and robust computing architectures.

neuroscience↗