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Music in the Lives of Deaf Children with Cochlear Implants

2009· article· en· W2003456551 on OpenAlexaff
Sandra E. Trehub, Tara Vongpaisal, Takayuki Nakata

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAudiologyCochlear implantPsychologyCochlear implantationMedicine

Abstract

fetched live from OpenAlex

Present-day cochlear implants provide good temporal cues and coarse spectral cues. In general, these cues are adequate for perceiving speech in quiet backgrounds and for young children's acquisition of spoken language. They are inadequate, however, for conveying the rich pitch-patterning of music. As a result, many adults who become implant users after losing their hearing find music disappointing or unacceptable. By contrast, child implant users who were born deaf or became deaf as infants or toddlers typically find music interesting and enjoyable. They recognize popular songs that they hear regularly when the test materials match critical features of the original versions. For example, they can identify familiar songs from the original recordings with words and from versions that omit the words but preserve all other cues. They also recognize theme songs from their favorite television programs when presented in original or somewhat altered form. The motivation of children with implants for listening to music or melodious speech is evident well before they understand language. Within months after receiving their implant, they prefer singing to silence. They also prefer speech in the maternal style to typical adult speech and the sounds of their native language-to-be to those of a foreign language. An important task of future research is to ascertain the relative contributions of perceptual and motivational factors to the apparent differences between child and adult implant users.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.107
GPT teacher head0.343
Teacher spread0.236 · 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

Citations56
Published2009
Admission routes1
Has abstractyes

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