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Record W4200560182 · doi:10.1017/s0305000921000684

Developmental change in children’s speech processing of auditory and visual cues: An eyetracking study

2021· article· en· W4200560182 on OpenAlexafffund
Tania S. Zamuner, Theresa Rabideau, Margarethe McDonald, H. Henny Yeung

Bibliographic record

VenueJournal of Child Language · 2021
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsSimon Fraser UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyModalitiesModality (human–computer interaction)Visual perceptionSpeech perceptionVisual processingAudiologyCognitive psychologyPerceptionComputer science

Abstract

fetched live from OpenAlex

= 29) use auditory and visual speech for word recognition. The goal was to bridge the gap between apparent successes of visual speech processing in young children in visual-looking tasks, with apparent difficulties of speech processing in older children from explicit behavioural measures. Participants were presented with familiar words in audio-visual (AV), audio-only (A-only) or visual-only (V-only) speech modalities, then presented with target and distractor images, and looking to targets was measured. Adults showed high accuracy, with slightly less target-image looking in the V-only modality. Developmentally, looking was above chance for both AV and A-only modalities, but not in the V-only modality until 6 years of age (earlier on /k/-initial words). Flexible use of visual cues for lexical access develops throughout childhood.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.384
Teacher spread0.353 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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