Hemispheric processing of pitch accent in Japanese by native and non-native listeners.
Bibliographic record
Abstract
It is well established that language processing is left hemisphere dominant. Previous findings, however, indicate that lateralization of different levels of linguistic prosody varies with their functional load as well as listeners' linguistic experience. This study explored the hemispheric processing of Japanese pitch accent by native and non-native listeners differing in experience with pitch, including 16 native Japanese participants, 16 Mandarin Chinese participants whose native language has linguistic tonal contrasts, and 16 English participants with no tone or pitch accent background. Pitch accent pairs were dichotically presented and the listeners were asked to identify which pitch accent pattern they heard in each ear. Preliminary results showed that for all the three groups, the percentage of errors for the left ear and that for the right ear were comparable, indicating no hemispheric dominance. The Japanese group did not reveal left hemisphere dominance, as previously found for linguistic tone processing by native listeners. The performance of Mandarin group infers that tone language background did not significantly affect the lateralization of pitch accent. These findings are discussed in terms of how linguistic function differentially influences the hemispheric specialization of different domains of prosodic processing by native and non-native listeners. [Work supported by the NSERC.]
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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 | 0.000 | 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".