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Record W2333728203 · doi:10.1037/a0038784

Enhanced processing of vocal melodies in childhood.

2015· article· en· W2333728203 on OpenAlexfundno aff
Michael W. Weiss, E. Glenn Schellenberg, Sandra E. Trehub, Emily J. Dawber

Bibliographic record

VenueDevelopmental Psychology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMelodyTimbreSingingPsychologyPianoAudiologyCommunicationDevelopmental psychologyAcousticsArtMusicalMedicine

Abstract

fetched live from OpenAlex

Music cognition is typically studied with instrumental stimuli. Adults remember melodies better, however, when they are presented in a biologically significant timbre (i.e., the human voice) than in various instrumental timbres (Weiss, Trehub, & Schellenberg, 2012). We examined the impact of vocal timbre on children's processing of melodies. In Study 1, 9- to 11-year-olds listened to 16 unfamiliar folk melodies (4 each of voice, piano, banjo, or marimba). They subsequently listened to the same melodies and 16 timbre-matched foils, and judged whether each melody was old or new. Vocal melodies were recognized better than instrumental melodies, which did not differ from one another, and the vocal advantage was consistent across age. In Study 2, 5- to 6-year-olds and 7- to 8-year-olds were tested with a simplified design that included only vocal and piano melodies. Both age groups successfully differentiated old from new melodies, but memory was more accurate for the older group. The older children recognized vocal melodies better than piano melodies, whereas the younger children tended to label vocal melodies as old whether they were old or new. The results provide the first evidence of differential processing of vocal and instrumental melodies in childhood.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.372
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.090
GPT teacher head0.358
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2015
Admission routes1
Has abstractyes

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