The effects of musicality and language background on cue integration in pitch perception
Bibliographic record
Abstract
Pitch perception involves the processing of multidimensional acoustic cues, and listeners can exhibit different cue integration strategies in interpreting pitch. This study aims to examine whether musicality and language experience have effects on listeners' pitch perception strategies. Both Mandarin and English listeners were recruited to participate in two experiments: (1) a pitch classification experiment that tested their relative reliance on f0 and spectral cues, and (2) the Montreal Battery of Evaluation of Musical Abilities that objectively quantified their musical aptitude as continuous musicality scores. Overall, the results show a strong musicality effect: Listeners with higher musicality scores relied more on f0 in pitch perception, while listeners with lower musicality scores were more likely to attend to spectral cues. However, there were no effects of language experience on musicality scores or cue integration strategies in pitch perception. These results suggest that less musical or even amusic subjects may not suffer impairment in linguistic pitch processing due to the multidimensional nature of pitch cues.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".