When language‐general and language‐specific processes are in conflict: The case of sub‐syllabic word segmentation in toddlers
Bibliographic record
Abstract
Infants use statistics-based word segmentation strategies from the preverbal stage. Statistical segmentation is, however, constrained by the Onset Bias, a language-universal principle that disfavors segmentation that harms syllable integrity. Children eventually learn language-specific exceptions to this principle. For instance, sub-syllabic parsing occurs for vowel-initial words in French liaison contexts, that is, when a word's final consonant surfaces as the following word's syllabic onset (e.g., /n/ in un /n/éléphant). In past research, French-learning 24-month-olds succeeded in parsing a vowel-initial pseudo-word surfacing with variable liaison consonants. This study further investigated infants' liaison representation, its potential impacts on parsing, and its interaction with the Onset Bias. In Experiments 1 and 2, French-learning 24-month-olds were familiarized with pseudo-words with variable liaison-like versus nonliaison-like onset consonants, preceded by words that cannot trigger those onsets (e.g., un zonche; un gonche). We found no mis-segmentation as vowel-initial and successful segmentation as consonant-initial. In Experiment 3, when the preceding words could trigger a liaison consonant that matched the onset of the following word (e.g., un nonche), infants showed a vowel-initial mis-interpretation, against the Onset Bias, revealing an effect of liaison knowledge. These results demonstrate that toddlers balance their use of language-general principles/strategies and language-specific knowledge during early acquisition.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".