bioRxivNeurosciencePreprint
Visual and auditory cortices represent acoustic speech-related information during silent lip reading
Abstract
Speech is an intrinsically multisensory signal and seeing the speakers lips forms a cornerstone of communication in acoustically impoverished environments. Still, it remains unclear how the brain exploits visual speech for comprehension and previous work debated whether lip signals are mainly processed along the auditory pathways or whether the visual system directly implements speech-related processes. To probe this question, we systematically characterized dynamic representations of multiple acoustic and visual speech-derived features in source localized MEG recordings that were obtained while participants listened to speech or viewed silent speech. Using a mutual-information framework we provide a comprehensive assessment of how well temporal and occipital cortices reflect the physically presented signals and speech-related features that were physically absent but may still be critical for comprehension. Our results demonstrate that both cortices are capable of a functionally specific form of multisensory restoration: during lip reading both reflect unheard acoustic features, with occipital regions emphasizing spectral information and temporal regions emphasizing the speech envelope. Importantly, the degree of envelope restoration was predictive of lip reading performance. These findings suggest that when seeing the speakers lips the brain engages both visual and auditory pathways to support comprehension by exploiting multisensory correspondences between lip movements and spectro-temporal acoustic cues. HighlightsO_LIVisual and auditory cortex represent unheard acoustic information during lip reading C_LIO_LIAuditory cortex emphasizes the acoustic envelope C_LIO_LIVisual cortex emphasizes a pitch signature C_LIO_LITracking of unheard features in auditory cortex is associated with behavior C_LI
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 2
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