MétaCan
Menu
Back to cohort
Record W4367173004 · doi:10.1080/09297049.2023.2202902

Links between musical beat perception and phonological skills for autistic children

2023· article· en· W4367173004 on OpenAlexaff
Charlotte Rimmer, Hadas Dahary, Eve‐Marie Quintin

Bibliographic record

VenueChild Neuropsychology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityCentre for Research on Brain Language and Music
Fundersnot available
KeywordsPsychologyAutismPerceptionPhonological awarenessCognitive psychologyMusicalAutistic spectrumLiteracyDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

Exploring non-linguistic predictors of phonological awareness, such as musical beat perception, is valuable for children who present with language difficulties and diverse support needs. Studies on the musical abilities of children on the autism spectrum show that they have average or above-average musical production and auditory processing abilities. This study aimed to explore the relationship between musical beat perception and phonological awareness skills of children on the autism spectrum with a wide range of cognitive abilities. A total of 21 autistic children between the ages of 6 to 11 years old (M = 8.9, SD = 1.5) with full scale IQs ranging from 52 to 105 (M = 74, SD = 16) completed a beat perception and a phonological awareness task. Results revealed that phonological awareness and beat perception are positively correlated for children on the autism spectrum. Findings lend support to the potential use of beat and rhythm perception as a screening tool for early literacy skills, specifically for phonological awareness, for children with diverse support needs as an alternative to traditional verbal tasks that tend to underestimate the potential of children on the autism spectrum.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.984
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.322
Teacher spread0.280 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueChild NeuropsychologySame topicNeuroscience and Music PerceptionFrench-language works237,207