Dataset Information

An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions.

ABSTRACT: Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results suggest that speech tracking does not have to rely only on the acoustics but could also exploit ongoing interactions between oscillations and constraints flowing from internal language models.

SUBMITTER: Ten Oever S

PROVIDER: S-EPMC8328513 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions.

Ten Oever Sanne S Martin Andrea E AE

eLife 20210802

Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including ...[more]

PMID: 34338196

Similar Datasets

Project description:The role of attention in speech comprehension is not well understood. We used fMRI to study the neural correlates of auditory word, pseudoword, and nonspeech (spectrally rotated speech) perception during a bimodal (auditory, visual) selective attention task. In three conditions, Attend Auditory (ignore visual), Ignore Auditory (attend visual), and Visual (no auditory stimulation), 28 subjects performed a one-back matching task in the assigned attended modality. The visual task, attending to rapidly presented Japanese characters, was designed to be highly demanding in order to prevent attention to the simultaneously presented auditory stimuli. Regardless of stimulus type, attention to the auditory channel enhanced activation by the auditory stimuli (Attend Auditory>Ignore Auditory) in bilateral posterior superior temporal regions and left inferior frontal cortex. Across attentional conditions, there were main effects of speech processing (word+pseudoword>rotated speech) in left orbitofrontal cortex and several posterior right hemisphere regions, though these areas also showed strong interactions with attention (larger speech effects in the Attend Auditory than in the Ignore Auditory condition) and no significant speech effects in the Ignore Auditory condition. Several other regions, including the postcentral gyri, left supramarginal gyrus, and temporal lobes bilaterally, showed similar interactions due to the presence of speech effects only in the Attend Auditory condition. Main effects of lexicality (word>pseudoword) were isolated to a small region of the left lateral prefrontal cortex. Examination of this region showed significant word>pseudoword activation only in the Attend Auditory condition. Several other brain regions, including left ventromedial frontal lobe, left dorsal prefrontal cortex, and left middle temporal gyrus, showed Attention x Lexicality interactions due to the presence of lexical activation only in the Attend Auditory condition. These results support a model in which neutral speech presented in an unattended sensory channel undergoes relatively little processing beyond the early perceptual level. Specifically, processing of phonetic and lexical-semantic information appears to be very limited in such circumstances, consistent with prior behavioral studies.

Dataset Information

An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions.

Publications

An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets