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Record W2139380752 · doi:10.1207/s15327078in0301_1

Infants' Ability to Learn Phonetically Similar Words: Effects of Age and Vocabulary Size

2002· article· en· W2139380752 on OpenAlexaff
Janet F. Werker, Christopher T. Fennell, Kathleen Corcoran, Christine L. Stager

Bibliographic record

VenueInfancy · 2002
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWord learningPsychologyVocabularyTask (project management)Word (group theory)Vocabulary developmentRepresentation (politics)Cognitive psychologyWord listLinguisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Abstract What do novice word learners know about the sound of words? Word‐learning tasks suggest that young infants (14 months old) confuse similar‐sounding words, whereas mispronunciation detection tasks suggest that slightly older infants (18–24 months old) correctly distinguish similar words. Here we explore whether the difficulty at 14 months stems from infants' novice status as word learners or whether it is inherent in the task demands of learning new words. Results from 3 experiments support a developmental explanation. In Experiment 1, infants of 20 months learned to pair 2 phonetically similar words to 2 different objects under precisely the same conditions that infants of 14 months (Experiment 2) failed. In Experiment 3, infants of 17 months showed intermediate, but still successful, performance in the task. Vocabulary size predicted word‐learning performance, but only in the younger, less experienced word learners. The implications of these results for theories of word learning and lexical representation are discussed.

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)0.009
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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

Citations434
Published2002
Admission routes1
Has abstractyes

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