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Rhythm, Meter, and Timing: The Heartbeat of Musical Development

2018· reference-entry· en· W2897909791 on OpenAlexaff
Laurel J. Trainor, Susan E. Marsh‐Rollo

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRhythmEntrainment (biomusicology)SynchronicityHeartbeatPsychologyPerceptionCognitive psychologyCommunicationSingingMIDIComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Many biological processes have rhythmic organization, including the perception and production of music. Rhythms organize information that unfolds over time; they aid in parsing that information into meaningful hierarchical groupings; and the regularities of rhythms enable prediction of, and preparation for, when important information will occur in the future. Expressive deviations from isochronous timing convey emphasis, emotion, and meaning. Young infants are sensitive to timing and rhythm in music but these abilities become much more sophisticated during childhood. In the beginning, timing characteristics of infant-directed singing relate to the communication of emotional information. Through development, children become enculturated to the rhythmic structures in their environment, develop the oscillatory brain processes to link auditory and motor aspects of entrainment, become able to entrain movements to auditory rhythms, and use the synchronicity of movements between people to help make judgments about social relationships and who to trust and befriend.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.004

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 head0.128
GPT teacher head0.316
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations17
Published2018
Admission routes1
Has abstractyes

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