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Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

2022· article· en· W4214539826 on OpenAlexafffund
Alejandro Pérez, Matthew H. Davis, Robin A. A. Ince, Hanna Zhang, Zhanao Fu, Melanie Lamarca, Matthew A. Lambon Ralph, Philip J. Monahan

Bibliographic record

VenueCognition · 2022
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
FundersMedical Research CouncilEuropean CommissionSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaWellcome Trust
KeywordsActive listeningPsychologyNeurocomputational speech processingSpeech productionSpeech perceptionSpeech recognitionSpeech errorMotor theory of speech perceptionIntelligibility (philosophy)PerceptionEntrainment (biomusicology)AudiologyCommunicationComputer scienceAcousticsRhythm

Abstract

fetched live from OpenAlex

⁠This study investigates the dynamics of speech envelope tracking during speech production, listening and self-listening. We use a paradigm in which participants listen to natural speech (Listening), produce natural speech (Speech Production), and listen to the playback of their own speech (Self-Listening), all while their neural activity is recorded with EEG. After time-locking EEG data collection and auditory recording and playback, we used a Gaussian copula mutual information measure to estimate the relationship between information content in the EEG and auditory signals. In the 2-10 Hz frequency range, we identified different latencies for maximal speech envelope tracking during speech production and speech perception. Maximal speech tracking takes place approximately 110 ms after auditory presentation during perception and 25 ms before vocalisation during speech production. These results describe a specific timeline for speech tracking in speakers and listeners in line with the idea of a speech chain and hence, delays in communication.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2022
Admission routes2
Has abstractyes

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