How training structure, working memory, and vocabulary size shape high variability phonetic training
Bibliographic record
Abstract
High variability phonetic training (HVPT) enhances L2 learners’ ability to perceive difficult contrasts, but learning outcomes vary depending on individual and training-related factors. This study examined whether individual differences in working memory and vocabulary size influence HVPT outcomes across different training structures. Seventy-nine Korean learners of English completed a pretest, five training sessions, an immediate posttest, and a delayed posttest 2 weeks later. Participants were randomly assigned to one of three conditions: 1-talker (n = 29), 6-talker blocked (n = 25), or 6-talker mixed (n = 25). Training involved word identification with feedback, targeting the English vowel contrasts /i/-/ɪ/ and /ɛ/-/æ/. Tests measured identification accuracy for trained and untrained items produced by a novel talker. Working memory was measured by digit span tasks; vocabulary size was assessed with a vocabulary size test. Mixed-effects modeling revealed a main effect of training (pre–post), with greater gains in the 6-talker conditions. Vocabulary size predicted accuracy overall, while working memory interacted with training structure: in the 1-talker condition, higher working memory was associated with larger gains. The mixed condition yielded more sustained improvement than the blocked condition, particularly among learners with higher vocabulary. These results suggest that cognitive and linguistic factors shape HVPT effectiveness depending on the structure of variability.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".