Effects of musical experience on linguistic pitch perception training
Bibliographic record
Abstract
Research continues to address the extent to which music ability transfers to other tasks and processes, among them speech perception. The current study examines the transfer of music experience to native and non-native linguistic pitch perception and tracks this process during training. Participants were nonmusicians (NMs) and music conservatory students (CMs), all of whom were native Norwegian listeners. Participants were tested with Norwegian and Mandarin materials in a tone-based and intonation-based directed attention dichotic listening task, administered five times during 2 weeks. Results show that training generally leads to increased accuracy in linguistic pitch perception. CMs are more accurate than NM in both tasks, and maintain this advantage throughout training. For both groups, training led to a decreased difference in performance between ears, with notably different patterns of ear improvement between NMs and CMs. Across languages, whereas CMs had improved accuracy in both tasks, NM showed no improvement in the tone task for either language. Findings indicate that musically trained listeners’ experience positively transfers to native and non-native linguistic pitch perception, an advantage which, in training, affords them more extensive improvement across linguistic pitch tasks and in particular for a non-native pitch task where the native linguistic system does not interfere.
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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 | 0.004 |
| 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 | 0.001 |
| 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".