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Record W2804509888 · doi:10.1111/nyas.13853

Musical rhythm and reading development: does beat processing matter?

2018· review· en· W2804509888 on OpenAlexaff
Ola Ozernov‐Palchik, Aniruddh D. Patel

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2018
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsRhythmBeat (acoustics)Reading (process)Speech recognitionPsychologyCognitive psychologyAudio signal processingSpeech processingSalientMusicalComputer scienceCommunicationLinguisticsArtificial intelligenceAudio signal

Abstract

fetched live from OpenAlex

There is mounting evidence for links between musical rhythm processing and reading-related cognitive skills, such as phonological awareness. This may be because music and speech are rhythmic: both involve processing complex sound sequences with systematic patterns of timing, accent, and grouping. Yet, there is a salient difference between musical and speech rhythm: musical rhythm is often beat-based (based on an underlying grid of equal time intervals), while speech rhythm is not. Thus, the role of beat-based processing in the reading-rhythm relationship is not clear. Is there is a distinct relation between beat-based processing mechanisms and reading-related language skills, or is the rhythm-reading link entirely due to shared mechanisms for processing nonbeat-based aspects of temporal structure? We discuss recent evidence for a distinct link between beat-based processing and early reading abilities in young children, and suggest experimental designs that would allow one to further methodically investigate this relationship. We propose that beat-based processing taps into a listener's ability to use rich contextual regularities to form predictions, a skill important for reading development.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.222
GPT teacher head0.401
Teacher spread0.179 · 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 designOther design
Domainnot available
GenreReview

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

Citations55
Published2018
Admission routes1
Has abstractyes

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