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Record W1502810912 · doi:10.1111/nyas.12622

Musical affect regulation in infancy

2015· article· en· W1502810912 on OpenAlexafffund
Sandra E. Trehub, Niusha Ghazban, Mariève Corbeil

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsUniversité de MontréalToronto Metropolitan UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAffect (linguistics)SingingDistractionActive listeningPsychologyArousalDistressAffect regulationDevelopmental psychologyMusicalCognitive psychologyCommunicationSocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Adolescents and adults commonly use music for various forms of affect regulation, including relaxation, revitalization, distraction, and elicitation of pleasant memories. Mothers throughout the world also sing to their infants, with affect regulation as the principal goal. To date, the study of maternal singing has focused largely on its acoustic features and its consequences for infant attention. We describe recent laboratory research that explores the consequences of singing for infant affect regulation. Such work reveals that listening to recordings of play songs can maintain 6- to 9-month-old infants in a relatively contented or neutral state considerably longer than recordings of infant-directed or adult-directed speech. When 10-month-old infants fuss or cry and are highly aroused, mothers' multimodal singing is more effective than maternal speech at inducing recovery from such distress. Moreover, play songs are more effective than lullabies at reducing arousal in Western infants. We explore the implications of these findings along with possible practical applications.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.279
GPT teacher head0.486
Teacher spread0.206 · 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 designObservational
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

Citations106
Published2015
Admission routes2
Has abstractyes

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