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Record W3123001101 · doi:10.3390/brainsci11010114

The Role of Audiovisual Speech in Fast-Mapping and Novel Word Retention in Monolingual and Bilingual 24-Month-Olds

2021· article· en· W3123001101 on OpenAlexafffund
Drew Weatherhead, Maria M. Arredondo, Loreto Nácar Garcia, Janet F. Werker

Bibliographic record

VenueBrain Sciences · 2021
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British ColumbiaDalhousie University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsReferentPsychologyWord (group theory)Task (project management)Word recognitionWord learningSpeech recognitionAudiologyCognitive psychologyLinguisticsComputer scienceReading (process)VocabularyMedicine

Abstract

fetched live from OpenAlex

Three experiments examined the role of audiovisual speech on 24-month-old monolingual and bilinguals’ performance in a fast-mapping task. In all three experiments, toddlers were exposed to familiar trials which tested their knowledge of known word–referent pairs, disambiguation trials in which novel word–referent pairs were indirectly learned, and retention trials which probed their recognition of the newly-learned word–referent pairs. In Experiment 1 (n = 48), lip movements were present during familiar and disambiguation trials, but not retention trials. In Experiment 2 (n = 48), lip movements were present during all three trial types. In Experiment 3 (bilinguals only, n = 24), a still face with no lip movements was present in all three trial types. While toddlers succeeded in the familiar and disambiguation trials of every experiment, success in the retention trials was only found in Experiment 2. This work suggests that the extra-linguistic support provided by lip movements improved the learning and recognition of the novel words.

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 score0.795
Threshold uncertainty score0.262

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 integrity0.0000.000
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 head0.064
GPT teacher head0.359
Teacher spread0.295 · 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

Citations21
Published2021
Admission routes2
Has abstractyes

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