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Record W4396527571 · doi:10.1016/j.bandl.2024.105415

Movement-related cortical potential and speech-induced suppression during speech production in younger and older adults

2024· article· en· W4396527571 on OpenAlexafffund
Pascale Tremblay, Marc Sato

Bibliographic record

VenueBrain and Language · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité Laval
FundersCanada Research ChairsCentre National de la Recherche ScientifiqueAix-Marseille Université
KeywordsSpeech productionPsychologyAudiologyNeurocomputational speech processingMotor controlVowelActive listeningAuditory feedbackSpeech perceptionNeuroscienceCommunicationSpeech recognitionPerceptionMedicine

Abstract

fetched live from OpenAlex

With age, the speech system undergoes important changes that render speech production more laborious, slower and often less intelligible. And yet, the neural mechanisms that underlie these age-related changes remain unclear. In this EEG study, we examined two important mechanisms in speech motor control: pre-speech movement-related cortical potential (MRCP), which reflects speech motor planning, and speaking-induced suppression (SIS), which indexes auditory predictions of speech motor commands, in 20 healthy young and 20 healthy older adults. Participants undertook a vowel production task which was followed by passive listening of their own recorded vowels. Our results revealed extensive differences in MRCP in older compared to younger adults. Further, while longer latencies were observed in older adults on N1 and P2, in contrast, the SIS was preserved. The observed reduced MRCP appears as a potential explanatory mechanism for the known age-related slowing of speech production, while preserved SIS suggests intact motor-to-auditory integration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.258
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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