Plasticity of the Human Auditory Cortex Induced by Discrimination Learning of Non-Native, Mora-Timed Contrasts of the Japanese Language
Bibliographic record
Abstract
In this magnetoencephalographic (MEG) study, we examined with high temporal resolution the traces of learning in the speech-dominant left-hemispheric auditory cortex as a function of newly trained mora-timing. In Japanese, the "mora" is a temporal unit that divides words into almost isochronous segments (e.g., na-ka-mu-ra and to-o-kyo-o each comprises four mora). Changes in the brain responses of a group of German and Japanese subjects to differences in the mora structure of Japanese words were compared. German subjects performed a discrimination training in 10 sessions of 1.5 h each day. They learned to discriminate Japanese pairs of words (in a consonant, anni-ani; and a vowel, kiyo-kyo, condition), where the second word was shortened by one mora in eight steps of 15 msec each. A significant increase in learning performance, as reflected by behavioral measures, was observed, accompanied by a significant increase of the amplitude of the Mismatch Negativity Field (MMF). The German subjects' hit rate for detecting durational deviants increased by up to 35%. Reaction times and MMF latencies decreased significantly across training sessions. Japanese subjects showed a more sensitive MMF to smaller differences. Thus, even in young adults, perceptual learning of non-native mora-timing occurs rapidly and deeply. The enhanced behavioral and neurophysiological sensitivity found after training indicates a strong relationship between learning and (plastic) changes in the cortical substrate.
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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 | 0.000 |
| 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".