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Record W2029973710 · doi:10.3389/fpsyg.2013.00372

Speech vs. singing: infants choose happier sounds

2013· article· en· W2029973710 on OpenAlexafffund
Mariève Corbeil, Sandra E. Trehub, Isabelle Peretz

Bibliographic record

VenueFrontiers in Psychology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de MontréalUniversity of TorontoInternational Laboratory for Brain, Music and Sound Research
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSingingLyricsPsychologyActive listeningTurkishAudiologyCommunicationLinguisticsAcousticsMedicine

Abstract

fetched live from OpenAlex

Infants prefer speech to non-vocal sounds and to non-human vocalizations, and they prefer happy-sounding speech to neutral speech. They also exhibit an interest in singing, but there is little knowledge of their relative interest in speech and singing. The present study explored infants' attention to unfamiliar audio samples of speech and singing. In Experiment 1, infants 4-13 months of age were exposed to happy-sounding infant-directed speech vs. hummed lullabies by the same woman. They listened significantly longer to the speech, which had considerably greater acoustic variability and expressiveness, than to the lullabies. In Experiment 2, infants of comparable age who heard the lyrics of a Turkish children's song spoken vs. sung in a joyful/happy manner did not exhibit differential listening. Infants in Experiment 3 heard the happily sung lyrics of the Turkish children's song vs. a version that was spoken in an adult-directed or affectively neutral manner. They listened significantly longer to the sung version. Overall, happy voice quality rather than vocal mode (speech or singing) was the principal contributor to infant attention, regardless of age.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.312
Teacher spread0.282 · 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 designNot applicable
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

Citations92
Published2013
Admission routes2
Has abstractyes

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