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Record W4220796721 · doi:10.1177/01427237221079137

Toddlers use functional morphemes for backward syntactic categorization

2022· article· en· W4220796721 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

VenueFirst Language · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMorphemeCategorizationMandarin ChineseLinguisticsNounSyntactic structurePsychologyProsodySyntaxWord (group theory)Natural language processingArtificial intelligenceComputer scienceSpeech recognition

Abstract

fetched live from OpenAlex

Previous studies show that infants store functional morphemes for inferring syntactic categories of adjacent words, and they generally perform better with nouns than with verbs. In this study, we tested whether toddlers can exploit phrasal groupings for syntactic categorization in the face of noisy co-occurrence patterns. Using a visual fixation procedure, we examined whether Mandarin-learning 19-month-olds can categorize word X to the left of functional morpheme a in a prosody-neutral 3-word sequence X- a-Y, where a structurally selects X (X and Y being unfamiliar words). Infants at 19 months were familiarized either with X- ye-Y (‘even X N Y V ’) or with X- le-Y (‘have X V -ed Y N ’). While le features a more mixed distribution than ye, 19-month-olds succeeded with both ye and le by preferring grammatical new contexts of X over ungrammatical ones, consistent with the hypothesis that phrasal groupings ([X a. . .]) support syntactic categorization. Our findings provide initial evidence for infants’ ability to capture functional morphemes for backward syntactic categorization.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.974

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)0.0270.000

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 head0.034
GPT teacher head0.272
Teacher spread0.238 · 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