Phoneme discrimination and mismatch negativity in English and Japanese speakers
Bibliographic record
Abstract
Neural templates for phonemes in one's native language are formed early in life; these can be modified but are difficult to form de novo. These can be examined with mismatch negativity (MMN). Three phonemic contrasts were presented to adult native English compared with Japanese speakers who acquired English later in life: vowels native to both languages (/i//iy/), consonant-vowel contrasts (/da//wa/) phonemic in both languages, and consonant-vowel contrasts phonemic in English but not in Japanese (/ra//la/). For vowels, no MMN differences were found. For /da//wa/, MMN amplitude was significantly reduced in Japanese speakers. For /ra//la/, only 50% of the Japanese group showed an identifiable MMN. This suggests that phonemic templates are formed early in life, and non-native consonant contrasts are difficult to learn later.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".