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Dynamic EEG analysis during language comprehension reveals interactive cascades between perceptual processing and sentential expectations.

ABSTRACT: Understanding spoken language requires analysis of the rapidly unfolding speech signal at multiple levels: acoustic, phonological, and semantic. However, there is not yet a comprehensive picture of how these levels relate. We recorded electroencephalography (EEG) while listeners (N = 31) heard sentences in which we manipulated acoustic ambiguity (e.g., a bees/peas continuum) and sentential expectations (e.g., Honey is made by bees). EEG was analyzed with a mixed effects model over time to quantify how language processing cascades proceed on a millisecond-by-millisecond basis. Our results indicate: (1) perceptual processing and memory for fine-grained acoustics is preserved in brain activity for up to 900 msec; (2) contextual analysis begins early and is graded with respect to the acoustic signal; and (3) top-down predictions influence perceptual processing in some cases, however, these predictions are available simultaneously with the veridical signal. These mechanistic insights provide a basis for a better understanding of the cortical language network.

SUBMITTER: Sarrett ME

PROVIDER: S-EPMC7682806 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Dynamic EEG analysis during language comprehension reveals interactive cascades between perceptual processing and sentential expectations.

Sarrett McCall E ME McMurray Bob B Kapnoula Efthymia C EC

Brain and language 20201018

Understanding spoken language requires analysis of the rapidly unfolding speech signal at multiple levels: acoustic, phonological, and semantic. However, there is not yet a comprehensive picture of how these levels relate. We recorded electroencephalography (EEG) while listeners (N = 31) heard sentences in which we manipulated acoustic ambiguity (e.g., a bees/peas continuum) and sentential expectations (e.g., Honey is made by bees). EEG was analyzed with a mixed effects model over time to quanti ...[more]

PMID: 33086178

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Project description:BackgroundSpeech intelligibility and speech comprehension for dysarthric speech has attracted much attention recently. Dysarthria is characterized by irregularities in the speed, strength, pitch, breath control, range, steadiness, and accuracy of muscle movements required for articulatory aspects of speech production.ObjectiveThis study examined the contributions made by other studies involved in dysarthric speech comprehension. We focused on the modes of meaning extraction used in generalizing speaker-listener underpinnings in light of semantic ontology extraction as a desired technique, applied method types, speech representations used, and databases sourced from.MethodsThis study involved a systematic literature review using 7 electronic databases: Cochrane Database of Systematic Reviews, Web of Science Core Collection, Scopus, PubMed, ACM, IEEE Xplore, and Google Scholar. The main eligibility criterion was the extraction of meaning from dysarthric speech using natural language processing or understanding approaches to improve on dysarthric speech comprehension. In total, out of 834 search results, 30 studies that matched the eligibility requirements were acquired following screening by 2 independent reviewers, with a lack of consensus being resolved through joint discussion or consultation with a third party. In order to evaluate the studies' methodological quality, the risk of bias assessment was based on the Cochrane risk-of-bias tool version 2 (RoB2) with 23 of the studies (77%) registering low risk of bias and 7 studies (33%) raising some concern over the risk of bias. The overall quality assessment of the study was done using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis).ResultsFollowing a review of 30 primary studies, this study revealed that the reviewed studies focused on natural language understanding or clinical approaches, with an increase in proposed solutions from 2020 onwards. Most studies relied on speaker-dependent speech features, while others used speech patterns, semantic knowledge, or hybrid approaches. The prevalent use of vector representation aligned with natural language understanding models, while Mel-frequency cepstral coefficient representation and no representation approaches were applied in neural networks. Hybrid representation studies aimed to reconstruct dysarthric speech or improve comprehension. Comprehensive databases, like TORGO and UA-Speech, were commonly used in combination with other curated databases, while primary data was preferred for specific or unique research objectives.ConclusionsWe found significant gaps in dysarthric speech comprehension characterized by the lack of inclusion of important listener or speech-independent features in the speech representations, mode of extraction, and data sources used. Further research is therefore proposed regarding the formulation of models that accommodate listener and speech-independent features through semantic ontologies that will be useful in the inclusion of key features of listener and speech-independent features for meaning extraction of dysarthric speech.

Dataset Information

Dynamic EEG analysis during language comprehension reveals interactive cascades between perceptual processing and sentential expectations.

Publications

Dynamic EEG analysis during language comprehension reveals interactive cascades between perceptual processing and sentential expectations.

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