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Record W4247522507 · doi:10.31234/osf.io/xjcr6

Eyes can tell. Attention to the eyes and the mouth during audiovisual vowel processing in monolingual and bilingual infants

2021· preprint· en· W4247522507 on OpenAlexafffund
Jovana Pejović, Eiling Yee, Mónika Molnár

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaEusko Jaurlaritza
KeywordsVowelAudiologyPsychologyConsonantLinguisticsMedicine

Abstract

fetched live from OpenAlex

After 6 months of age monolingual infants look more to the mouth of a speaker when there is a mismatch (as opposed to match) between heard and (visually) articulated native consonants. Here, we examined whether monolingual and bilingual infants increase their attention to speakers’ mouth when processing vowels. We compared 4.5- and 8-month-old monolingual and bilingual infants’ attention to the eyes and the mouth while presented with native vowels in audiovisual match and mismatch conditions. We observed that 4.5-month-old monolingual and bilingual infants detect the AV mismatch by increasing their attention to the eyes, not to the mouth – as has been previously observed for consonants. However, by 8 months of age monolingual and bilingual infants’ attention to the eyes and the mouth is not affected by audiovisual disruption. Our findings suggest that audiovisual vowel and consonant processing differ during the first year of life, and that the specific type of linguistic experience does not modulate selective attention to the mouth or the eyes during vowel processing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.031
GPT teacher head0.357
Teacher spread0.326 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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