The Effect of L1-L2 Vowel Category Mapping on L2 Word Learning: English Speakers Learning Arabic Pseudo-Words
Bibliographic record
Abstract
Adult language learners often face challenges perceiving unfamiliar speech sounds.Models of second language (L2) speech perception suggest that adults learn L2 through the "filter" of their first language (L1), frequently resulting in misperception and the production of accented speech.The present study had two goals: 1) to examine the learning of a demanding L2 speech contrast by English listeners, and 2) to investigate the role of cognitive resources when learning novel phoneme categories.The first goal was achieved by asking English listeners to learn Arabic vowels embedded in word-like contexts.Unlike English, Arabic uses vowel length as a primary acoustic cue to vowels.English speakers who had no experience with Arabic were presented 36 pseudo-words containing Arabic vowels in a word-learning experiment.The stimuli contained three Arabic short vowels /i,u,a/ and their long counterparts.The task required listeners to learn to associate the pseudo-words with complex images of non-existent objects.The second goal of the study was achieved by looking at the relationship between learning performance and two types of cognitive resources, working memory and attention.Working memory was measured using a span task and the attention was measured using the Attention Network Task.Results yielded no statistically significant effect of vowel type or length on word learnability, nor did participants' performance on the word-learning task improve significantly over five learning blocks.A small, nonsignificant positive relationship between attentional capacity and overall performance was observed.The results indicate that although listeners may have been able to perceive the difference between the Arabic vowels, learning to associate the novel phonemes to novel concepts may have been too difficult for participants.Suggestions for future work are discussed.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".