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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".