Effects of acoustic and linguistic experience on Japanese pitch accent processing
Bibliographic record
Abstract
This study investigated the effects of L2 learning experience in relation to L1 background on hemispheric processing of Japanese pitch accent. Native Mandarin Chinese (tonal L1) and English (non-tonal L1) learners of Japanese were tested using dichotic listening. These listener groups were compared with those recruited in Wu, Tu & Wang (2012), including native Mandarin and English listeners without Japanese experience and native Japanese listeners. Results revealed an overall right-hemisphere preference across groups, suggesting acoustically oriented processing. Individual pitch accent patterns also revealed pattern-specific laterality differences, further reflecting acoustic-level processing. However, listener group differences indicated L1 effects, with the Chinese but not English listeners approximating the Japanese patterns. Furthermore, English learners but not naïve listeners exhibited a shift towards the native direction, revealing effects of L2 learning. These findings imply integrated effects of acoustic and linguistic aspects on Japanese pitch accent processing as a function of L1 and L2 experience.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".