MétaCan
Menu
Back to cohort
Record W4362639996 · doi:10.1080/15248372.2023.2197067

English-Learning 12-Month-Olds Do Not Map Function-Like Words to Objects

2023· article· en· W4362639996 on OpenAlexafffundabout
Susan Geffen, Suzanne Curtin, Susan A. Graham

Bibliographic record

VenueJournal of Cognition and Development · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaAlberta Children's Hospital Foundation
KeywordsPsychologyObject (grammar)Set (abstract data type)Language acquisitionWord (group theory)Function (biology)LinguisticsTask (project management)Natural language processingCognitive psychologyArtificial intelligenceComputer scienceMathematics education

Abstract

fetched live from OpenAlex

By 12 months, English-learning infants have an awareness of the sound patterns of word forms that constitute acceptable labels for objects in their native language. In the following experiments, we replicated and extended previous findings that Canadian English-learning infants will not link function-like words with novel objects. Across three experiments using the Switch task, 101 infants living in Calgary, Canada, were habituated to two CV and VC word-object pairings. At test, infants did not look longer on the Switch trial and the Same trial, suggesting they did not form word-object associations between prototypical function words and a novel object in any of the experiments (ps>0.5). This set of null results extends prior research showing that Canadian English-learning infants will not link function-like words with novel objects and suggests that infants’ prior experience with their native language may constrain their learning of novel labels.

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 score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.275
Teacher spread0.255 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Cognition and DevelopmentSame topicLanguage Development and DisordersFrench-language works237,207