Listeners learn phonotactic patterns conditioned on suprasegmental cues
Bibliographic record
Abstract
Language learners are sensitive to phonotactic patterns from an early age, and can acquire both simple and 2nd-order positional restrictions contingent on segment identity (e.g., /f/ is an onset with /æ/but a coda with /ɪ/). The present study explored the learning of phonototactic patterns conditioned on a suprasegmental cue: lexical stress. Adults first heard non-words in which trochaic and iambic items had different consonant restrictions. In Experiment 1, participants trained with phonotactic patterns involving natural classes of consonants later falsely recognized novel items that were consistent with the training patterns (legal items), demonstrating that they had learned the stress-conditioned phonotactic patterns. However, this was only true for iambic items. In Experiment 2, participants completed a forced-choice test between novel legal and novel illegal items and were again successful only for the iambic items. Experiment 3 demonstrated learning for trochaic items when they were presented alone. Finally, in Experiment 4, in which the training phase was lengthened, participants successfully learned both sets of phonotactic patterns. These experiments provide evidence that learners consider more global phonological properties in the computation of phonotactic patterns, and that learners can acquire multiple sets of patterns simultaneously, even contradictory ones.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".