Can Phonological Working Memory Be Improved Through Perceptual Training?
Bibliographic record
Abstract
This study investigates whether phonological working memory (PWM) can be improved through perceptual training, specifically high variability phonetic training (HVPT). PWM plays a critical role in second language (L2) acquisition, influencing vocabulary size, grammatical ability, and speech perception. Twenty native Farsi-speaking females (mean age: 24), enrolled in upper- intermediate to advanced English courses in Iran, participated in this quantitative study. Pre- and post-test serial minimal-pair recognition tasks, which included both real words and non-words in hVd frames, were administered using the Gorilla platform to assess PWM, alongside forced- choice identification tasks for 10 English vowels. Participants completed eight HVPT sessions on English Accent Coach (www.englishaccentcoach.com). Results showed significant improvements in both vowel recognition and PWM capacity following training, suggesting that cognitive processing and L2 fluency were enhanced through targeted perceptual interventions. This study provides empirical evidence supporting the connection between perceptual training and PWM, highlighting implications for more effective language teaching strategies and L2 development.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".