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Record W2116465859 · doi:10.3389/fnhum.2014.00488

Music Lessons Improve Auditory Perceptual and Cognitive Performance in Deaf Children

2014· article· en· W2116465859 on OpenAlexaff
Françoise Rochette, Aline Moussard, Emmanuel Bigand

Bibliographic record

VenueFrontiers in Human Neuroscience · 2014
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of TorontoBaycrest Hospital
Fundersnot available
KeywordsPsychologyAuditory perceptionCognitionPerceptionWorking memoryCognitive psychologySpeech perceptionAuditory scene analysisReading (process)Auditory imageryAudiologyEchoic memoryLinguistics

Abstract

fetched live from OpenAlex

Despite advanced technologies in auditory rehabilitation of profound deafness, deaf children often exhibit delayed cognitive and linguistic development and auditory training remains a crucial element of their education. In the present cross-sectional study, we assess whether music would be a relevant tool for deaf children rehabilitation. In normal-hearing children, music lessons have been shown to improve cognitive and linguistic-related abilities, such as phonetic discrimination and reading. We compared auditory perception, auditory cognition, and phonetic discrimination between 14 profoundly deaf children who completed weekly music lessons for a period of 1.5-4 years and 14 deaf children who did not receive musical instruction. Children were assessed on perceptual and cognitive auditory tasks using environmental sounds: discrimination, identification, auditory scene analysis, auditory working memory. Transfer to the linguistic domain was tested with a phonetic discrimination task. Musically trained children showed better performance in auditory scene analysis, auditory working memory and phonetic discrimination tasks, and multiple regressions showed that success on these tasks was at least partly driven by music lessons. We propose that musical education contributes to development of general processes such as auditory attention and perception, which, in turn, facilitate auditory-related cognitive and linguistic processes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.464

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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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 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

Citations67
Published2014
Admission routes1
Has abstractyes

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