Cross-domain correlation in pitch perception, the influence of native language
Bibliographic record
Abstract
The current study explores how language experience may shape the correlation between lexical tone and musical pitch perception. A two domains (music and lexical tone) by two languages (tone, Mandarin Chinese and non-tone, Dutch) design is adopted. Participants were tested on their discrimination of Mandarin Chinese lexical tones, Montreal Battery of Evaluation of Amusia (MBEA), and Musical Ear Test (MET). The Chinese listeners outperformed the Dutch listeners on both MBEA and MET, but had comparable accuracies for the lexical tone discrimination. Importantly, a significant cross-domain correlation was only observed for the Dutch listeners but not for the Chinese listeners. For tone language listeners, once lexical tones have been acquired, native listeners perceive them as phonological categories, and split them from other pitch variations that do not play a phonemic role. Non-tone language listeners, on the other hand, perceive both lexical tones and musical pitch on a psycho-acoustical basis, hence exhibit a unified perception of pitch across the two domains.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| 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".