The role of language experience in word segmentation: A comparison of English, French, and bilingual infants
Bibliographic record
Abstract
English-learning and French-learning 7.5-month-old infants were tested using the headturn preference procedure on their ability to segment bisyllabic words in both English and French. In the English condition infants were familiarized with trochaic bisyllables, the predominant stress pattern found in English, and then presented English passages with and without the familiarized words. In the French condition, infants were familiarized with iambic bisyllables, the characteristic word stress pattern found in French, and were then presented French passages with and without the familiarized words. Findings indicate that by 7.5 months of age, infants’ learning either a syllable-timed (French) or a stressed-timed language (English) can segment bisyllabic words with the predominant stress pattern of their native language. However, French infants fail to segment English trochaic words from English passages; data on English infants’ segmentation in the French condition are forthcoming. If both groups fail to segment in a rhythmically different non-native language, it will confirm that word segmentation abilities of 7.5-month-old infants are influenced by the prosodic structure of the native language. Preliminary results obtained from infants who are regularly exposed to both languages will also be reported.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".