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Record W3143493113 · doi:10.1017/9781316882764.010

Relating Neural Oscillations to Syllable Cycles and Chunks

2021· book-chapter· en· W3143493113 on OpenAlexaff
Victor J. Boucher

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEntrainment (biomusicology)Chunking (psychology)SyllableSpeech recognitionAlternation (linguistics)Computer scienceRhythmArtificial intelligencePhysicsAcousticsLinguistics

Abstract

fetched live from OpenAlex

The entrainment of neural oscillations to attributes of signals provides a key principle by which one can evaluate how the brain interfaces with structures of motor speech. For many authors, frequency-specific entrainment of delta (< 3 Hz) and theta (4–10 Hz ) oscillations to groups and syllable-size energy modulations define processing frames. However, there is little agreement on the type of information that is processed in the frames. A review is provided of diverging views on the role of entrainment and controversial claims that oscillations entrain to non-sensory units like words and phrases. A critical experiment is presented showing that, whereas theta oscillations entrain to acoustic attributes even in sequences of tones, delta entrains specifically to signature marks of chunking in speech stimuli regardless of whether the stimuli are meaningful utterances or meaningless series of syllables. By this evidence, delta waves do not entrain primarily to putative syntactic units but more generally to chunks of articulated sounds, which is consistent with a body of evidence demonstrating that chunking is a domain-general principle involved in processing motor sequences.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.008

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 head0.039
GPT teacher head0.227
Teacher spread0.188 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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