Representations for Phonotactic Learning in Infancy
Bibliographic record
Abstract
Infants rapidly learn novel phonotactic constraints from brief listening experience. Four experiments explored the nature of the representations underlying this learning. 16.5- and 10.5-month-old infants heard training syllables in which particular consonants were restricted to particular syllable positions (first-order constraints) or to syllable positions depending on the identity of the adjacent vowel (second-order constraints). Later, in a headturn listening-preference task, infants were presented with new syllables that either followed the experimental constraints or violated them. Infants at both ages learned first- and second-order constraints on consonant position (Experiments 1 and 2) but found second-order constraints more difficult to learn (Experiment 2). Infants also spontaneously generalized first-order constraints to syllables containing a new, transfer vowel; they did so whether the transfer vowel was similar to the familiarization vowels (Experiment 3), or dissimilar from them (Experiment 4). These findings suggest that infants recruit representations of individuated segments during phonological learning. Furthermore, like adults, they represent phonological sequences in a flexible manner that allows them to detect patterns at multiple levels of phonological analysis.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".