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Music as a scaffold for listening to speech: Better neural phase-locking to song than speech

2020· article· en· W3012882076 on OpenAlexafffund
Christina M. Vanden Bosch der Nederlanden, Marc F. Joanisse, Jessica A. Grahn

Bibliographic record

VenueNeuroImage · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRhythmSpeech recognitionActive listeningSyllableEntrainment (biomusicology)Computer scienceUtterancePhase lockingSpeech processingPsychologyCommunicationAcousticsPhase (matter)Physics

Abstract

fetched live from OpenAlex

Neural activity synchronizes with the rhythmic input of many environmental signals, but the capacity of neural activity to entrain to the slow rhythms of speech is particularly important for successful communication. Compared to speech, song has greater rhythmic regularity, a more stable fundamental frequency, discrete pitch movements, and a metrical structure, this may provide a temporal framework that helps listeners neurally track information better than the rhythmically irregular rhythms of speech. The current study used EEG to examine whether entrainment to the syllable rate of linguistic utterances, as indexed by cerebro-acoustic phase coherence, was greater when listeners heard sung than spoken sentences. We assessed listeners phase-locking in both easy (no time compression) and hard (50% time-compression) utterance conditions. Adults phase-locked equally well to speech and song in the easy listening condition. However, in the time-compressed condition, phase-locking was greater for sung than spoken utterances in the theta band (3.67-5 ​Hz). Thus, the musical temporal and spectral characteristics of song related to better phase-locking to the slow phrasal and syllable information (4-7 ​Hz) in the speech stream. These results highlight the possibility of using song as a tool for improving speech processing in individuals with language processing deficits, such as dyslexia.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.110
GPT teacher head0.339
Teacher spread0.230 · 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.

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

Citations74
Published2020
Admission routes2
Has abstractyes

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