Rapid gains in segmenting fluent speech when words match the rhythmic unit: evidence from infants acquiring syllable-timed languages
Bibliographic record
Abstract
The ability to extract word-forms from sentential contexts represents an initial step in infants' process toward lexical acquisition. By age 6 months the ability is just emerging and evidence of it is restricted to certain testing conditions. Most research has been developed with infants acquiring stress-timed languages (English, but also German and Dutch) whose rhythmic unit is not the syllable. Data from infants acquiring syllable-timed languages are still scarce and limited to French (European and Canadian), partially revealing some discrepancies with English regarding the age at which word segmentation ability emerges. Research reported here aims at broadening this cross-linguistic perspective by presenting first data on the early ability to segment monosyllabic word-forms by infants acquiring Spanish and Catalan. Three different language groups (two monolingual and one bilingual) and two different age groups (8- and 6-month-old infants) were tested using natural language and a modified version of the HPP with familiarization to passages and testing on words. Results revealed positive evidence of word segmentation in all groups at both ages, but critically, the pattern of preference differed by age. A novelty preference was obtained in the older groups, while the expected familiarity preference was only found at the younger age tested, suggesting more advanced segmentation ability with an increase in age. These results offer first evidence of an early ability for monosyllabic word segmentation in infants acquiring syllable-timed languages such as Spanish or Catalan, not previously described in the literature. Data show no impact of bilingual exposure in the emergence of this ability and results suggest rapid gains in early segmentation for words that match the rhythm unit of the native language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".