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Record W2141994663 · doi:10.1207/s15327078in1002_5

Recognition and Representation of Function Words in English‐Learning Infants

2006· article· en· W2141994663 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInfancy · 2006
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British ColumbiaUniversité du Québec à Montréal
Fundersnot available
KeywordsFunctorNonsenseVocabularyPsychologyRepresentation (politics)Function (biology)LinguisticsWord (group theory)KannadaCognitive psychologyMathematicsPure mathematicsArtificial intelligenceComputer scienceChemistry

Abstract

fetched live from OpenAlex

We examined infants' recognition of functors and the accuracy of the representations that infants construct of the perceived word forms. Auditory stimuli were “Functor + Content Word” versus “Nonsense Functor + Content Word” sequences. Eight‐, 11‐, and 13‐month‐old infants heard both real functors and matched nonsense functors (prosodically analogous to their real counterparts but containing a segmental change). Results reveal that 13‐month‐olds recognized functors with attention to segmental detail. Eight‐month‐olds did not distinguish real versus nonsense functors. The performance of 11‐month‐olds fell in between that of the older and younger groups, consistent with an emerging recognition of real functors. The three age groups exhibited a clear developmental trend. We propose that in the earliest stages of vocabulary acquisition, function elements receive no segmentally detailed representations, but such representations are gradually constructed so that once vocabulary growth starts in earnest, fully specified functor representations are in place to support it.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.539

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)0.0000.000

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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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