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Record W2954582667 · doi:10.3390/brainsci9070157

Poor Synchronization to Musical Beat Generalizes to Speech

2019· article· en· W2954582667 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBrain Sciences · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de MontréalMcGill UniversityInternational Laboratory for Brain, Music and Sound Research
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRhythmBeat (acoustics)Entrainment (biomusicology)Speech recognitionPsychologyCommunicationMusicalAudiologyComputer scienceAcousticsArtMedicinePhysics

Abstract

fetched live from OpenAlex

The rhythmic nature of speech may recruit entrainment mechanisms in a manner similar to music. In the current study, we tested the hypothesis that individuals who display a severe deficit in synchronizing their taps to a musical beat (called beat-deaf here) would also experience difficulties entraining to speech. The beat-deaf participants and their matched controls were required to align taps with the perceived regularity in the rhythm of naturally spoken, regularly spoken, and sung sentences. The results showed that beat-deaf individuals synchronized their taps less accurately than the control group across conditions. In addition, participants from both groups exhibited more inter-tap variability to natural speech than to regularly spoken and sung sentences. The findings support the idea that acoustic periodicity is a major factor in domain-general entrainment to both music and speech. Therefore, a beat-finding deficit may affect periodic auditory rhythms in general, not just those for music.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.240
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.047
GPT teacher head0.319
Teacher spread0.272 · 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