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Record W4214602325 · doi:10.3389/fpsyg.2022.740271

Visual Influence on Auditory Perception of Vowels by French-Speaking Children and Adults

2022· article· en· W4214602325 on OpenAlexafffund
Paméla Trudeau-Fisette, Laureline Arnaud, Lucie Ménard

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and MusicUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCategorizationPerceptionVowelSpeech perceptionContrast (vision)Sensory systemVisual perceptionAudiologyAuditory perceptionCognitive psychologyArticulation (sociology)PhoneticsSpeech recognitionLinguistics

Abstract

fetched live from OpenAlex

Audiovisual interaction in speech perception is well defined in adults. Despite the large body of evidence suggesting that children are also sensitive to visual input, very few empirical studies have been conducted. To further investigate whether visual inputs influence auditory perception of phonemes in preschoolers in the same way as in adults, we conducted an audiovisual identification test. The auditory stimuli (/e/-/ø/ continuum) were presented either in an auditory condition only or simultaneously with a visual presentation of the articulation of the vowel /e/ or /ø/. The results suggest that, although all participants experienced visual influence on auditory perception, substantial individual differences exist in the 5- to 6-year-old group. While additional work is required to confirm this hypothesis, we suggest that auditory and visual systems are developing at that age and that multisensory phonological categorization of the rounding contrast took place only in children whose sensory systems and sensorimotor representations were mature.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.998

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)0.0030.000

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 head0.010
GPT teacher head0.320
Teacher spread0.310 · 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.

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
Published2022
Admission routes2
Has abstractyes

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