Is prosodic information alone sufficient for guiding early grammatical acquisition?
Bibliographic record
Abstract
An infant perceptual experiment investigated the role of prosody. All-nonsense-word sentences (e.g., Guin felli crale vur ti gosine), each in structure 1 ([[Determiner + Adjective + Noun] [Verb + Determiner + Noun]]) and structure 2 ([[Determiner + Noun] [Verb + Preposition + Determiner + Noun]]), were recorded (by mimicking real-word French sentences) with disambiguating prosodic groupings matching the two major constituents. French-learning 20- and 24-month-olds were familiarized with either structure 1 or structure 2. All infants were tested with noun-use trials (e.g., Le crale "the crale-Noun") versus verb-use trials (Tu crales "You crale-Verb"). Structure-2-familiarized infants, but not structure-1-familiarized infants, discriminated the test trials, demonstrating that prosody alone guides verb categorization. Noun categorization requires determiners, as shown in earlier work [S. Massicotte-Laforge and R. Shi, J. Acoust. Soc. Am. 138(4), EL441-EL446 (2015)].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".