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Fourteen‐month‐old infants learn similar‐sounding words

2009· article· en· W2111942508 on OpenAlexaff
Katherine A. Yoshida, Christopher T. Fennell, Daniel Swingley, Janet F. Werker

Bibliographic record

VenueDevelopmental Science · 2009
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of OttawaUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyTask (project management)PhoneticsPerceptionDepth soundingLanguage acquisitionWord learningWord (group theory)Cognitive psychologySpeech perceptionLinguisticsVocabulary

Abstract

fetched live from OpenAlex

Can infants, in the very first stages of word learning, use their perceptual sensitivity to the phonetics of speech while learning words? Research to date suggests that infants of 14 months cannot learn two similar-sounding words unless there is substantial contextual support. The current experiment advances our understanding of this failure by testing whether the source of infants' difficulty lies in the learning or testing phase. Infants were taught to associate two similar-sounding words with two different objects, and tested using a visual choice method rather than the standard Switch task. The results reveal that 14-month-olds are capable of learning and mapping two similar-sounding labels; they can apply phonetic detail in new words. The findings are discussed in relation to infants' concurrent failure, and the developmental transition to success, in the Switch task.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.019
GPT teacher head0.304
Teacher spread0.286 · 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

Citations169
Published2009
Admission routes1
Has abstractyes

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