Distributional cues and the onset bias in early word segmentation.
Bibliographic record
Abstract
In previous infant studies on statistics-based word segmentation, the unit of statistical computation was always aligned with the syllabic edge, which had a consonant onset. The current study addressed whether the learning system imposes a constraint that favors word forms beginning with a consonant onset over those beginning with an onsetless sub-syllable, by examining infants' segmentation of vowel-initial non-words in French liaison. French-learning 20- and 24-month-old infants (N = 64) were familiarized with sentences containing variable liaison consonants preceding the same vowel-initial non-word (e.g., /n/onche, /z/onche, /r/onche, /t/onche), such that the distributional cues supported the sub-syllabic target (e.g., onche). After familiarization, we tested sub-syllabic statistical segmentation by presenting the vowel-initial target (e.g., onche) versus another non-familiarized vowel-initial word (e.g., èque). Another group of infants was tested with a consonant-initial mis-segmentation of the target (e.g., zonche) versus another non-familiarized consonant-initial word (e.g., zèque). Results showed that 20-month-olds failed to segment the vowel-initial targets, but they mis-segmented the targets as consonant-initial, indicating that the onset bias dominated over sub-syllabic statistics for word segmentation at this age. Twenty-four-month-olds showed ambiguous interpretations (i.e., both vowel-initial segmentation and consonant-initial mis-segmentation), suggesting that the use of statistics to segment sub-syllabic words was emerging while the onset bias continued to have an impact.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".