Effects of acoustic and linguistic aspects on Japanese pitch accent processing
Bibliographic record
Abstract
This study investigates the hemispheric processing of Japanese pitch accent by native and non-native listeners. The non-natives differ in their first (L1) and second (L2) language experience with prosodic pitch, including Mandarin (tonal L1) and English (non-tonal L1) listeners with or without Japanese learning experience. All listeners completed a dichotic listening test in which minimal pairs differing in pitch accent were presented. Overall, the results demonstrate a right hemisphere lateralization across groups, indicating holistic processing of temporal cues as the pitch accent patterns span across disyllabic domain. Moreover, the three pitch accent patterns reveal different degrees of hemispheric dominance, presumably attributable to the acoustic cues to each pattern which involve different hemispheric asymmetries. The results also reveal group difference, reflecting the effects of linguistic experience. Specifically, the English listeners with no Japanese background, compared to the other groups, exhibit greater variance in hemispheric dominance as a function of pitch accent difference, showing a greater reliance on acoustic cues when linguistic information is lacking. Together, the findings suggest an interplay of acoustic and linguistic aspects in the processing of Japanese pitch accent but showing a more prominent acoustic influence. [Research supported by NSERC.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".