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The effect of functional morphemes on word segmentation in preverbal infants

2008· article· en· W2121975023 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDevelopmental Science · 2008
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMorphemeNounFunctorPsychologyLinguisticsMean length of utteranceNonsensePart of speechNatural language processingArtificial intelligenceLanguage developmentComputer scienceMathematicsDevelopmental psychologyChemistryPure mathematics

Abstract

fetched live from OpenAlex

This study examines the role of functional morphemes in the earliest stage of lexical development. Recent research showed that prelinguistic infants can perceive functional morphemes. We inquire whether infants use frequent functors to segment potential word forms. French-learning 8-month-olds were familiarized to two utterance types: a novel noun following a functor, and another novel noun following a prosodically matched nonsense functor. After familiarization, infants' segmentation of the two nouns was assessed in a test phase presenting the nouns in isolation. Infants in Experiment 1 showed evidence of using both frequent functors des and mes (as opposed to the nonsense functor kes) to segment the nouns, suggesting also that they had specific representations of the functors. The infrequent functor vos in Experiment 2 did not facilitate segmentation. Frequency is thus a crucial factor. Our findings demonstrate that frequent functors can bootstrap infants into early lexical learning. Furthermore, the effect of functors for initial word segmentation is likely universal.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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