When language‐general and language‐specific processes are in conflict: The case of sub‐syllabic word segmentation in toddlers
Why this work is in the frame
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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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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 | 0.000 | 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 | 0.000 |
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 it