How do non-native phonemes impact learning words in a second language? Evidence from eyetracking and EEG in a laboratory word learning study
Bibliographic record
Abstract
A classic finding holds that listeners have significant difficulty categorizing and discriminating unfamiliar/nonnative phonemes. In the present study we examined how this influences learning new words in a second language (L2). Adult monolingual English speakers were trained on a pseudo-French vocabulary, by matching images of cartoon “aliens” to auditory CVCV words incorporating French vowels and consonants. Of interest was comparing words incorporating vowels similar to English to those containing highly unfamiliar vowels (here, the French high front rounded vowel [y]). Accuracy, eyetracking and event-related potentials (ERPs, measured with EEG) were then used to assess word recognition post-training. The neurocognitive measures indicated weakened recognition of words containing the novel [y] vowel, compared to words with vowels that more closely resembled those in English. Furthermore, we found that a training regime that emphasized discriminating easily confused vowels (i.e., [u] vs. [y]) during learning yielded somewhat improved recognition, both immediately after training and in a follow-up session. Interestingly, learning words containing the unfamiliar [y] vowel was not accompanied by improved AX discrimination of this vowel. The results have key implications for how we understand the role of phonology in L2 word representations, and for how we approach L2 teaching.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".