Hemispheric processing of tones by listeners with or without tone experience.
Bibliographic record
Abstract
Tones are used phonemically in many languages such as Mandarin, Thai, and Norwegian. Previous research [Wang et al. (2004)] revealed left hemispheric preference for Mandarin listeners in the processing of native tones, but no preference for either Norwegian or English listeners, even though the latter two differed in familiarity with tones. The current study examined hemispheric processing of Mandarin tones by 20 native listeners each of Mandarin, Thai, and English. The Thai listeners had experience only with Thai tones, while the English listeners had no tone experience. In a two-response dichotic listening paradigm, listeners reported the tone they heard in each ear. The pooled results across tones indicated left hemispheric preference for the Mandarin listeners, no hemispheric preference for the Thai listeners, and right hemispheric preference for the English listeners. The findings suggest that native tone experience may increase left hemisphere involvement in the processing of tones in another tone language, whereas lack of tone experience in the native language tends to be linked to non-linguistic hemispheric lateralization of tone processing. [Work supported by SSHRC.]
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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) | 0.004 | 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".