Phoneme and Stress Programming Interact During Nonword Repetition Learning
Bibliographic record
Abstract
Purpose Lexical stress and phoneme processes converge during phonological encoding, but the nature of the convergence has been debated. Stress patterns and phonemes may be integrated automatically and rigidly, resulting in a unified representation. Alternatively, stress and phoneme may be processed interactively based on sublexical contexts. The purpose of this study was to evaluate the extent to which the lexical stress and phoneme processing interact in a novel nonword learning paradigm. Method Twenty-seven adults with typical speech skills were trained to produce nonwords with specific phonemes, syllables, and stress patterns (Set 1) to an accuracy criterion. Then, participants repeated nonwords that varied from Set 1 in syllable position (Set 2), phoneme sequence (Set 3), included new phonemes (Set 4), or had new phonemes and stress patterns (Set 5). Nonword productions were perceptually analyzed, and phoneme and stress errors were counted. Results Participants' produced Set 1 nonwords with few phonemic or stress errors after training; a similar number of both types of errors were produced when comparing Sets 2 and 3. Greater phoneme and stress errors were produced on nonwords from Sets 4 and 5 compared to Sets 1-3. The highest number of phonemic errors occurred in Set 4 nonwords. There was no difference in the number of stress errors produced on nonwords in Sets 4 and 5. Conclusion The results of this study suggested that lexical stress and phoneme processing co-occurred and interacted during nonword productions. Trained stress patterns were learned during training; however, no evidence for a unified representation was observed. Negative interference was observed in nonwords with new phonemes and trained stress patterns, suggesting online phoneme processing may have dominated and interfered with the retrieval of stored metrical frames.
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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 | 0.004 |
| 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 | 0.001 | 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".