Pitch and Time Processing in Speech and Tones: The Effects of Musical Training and Attention
Bibliographic record
Abstract
Purpose: Musical training is often linked to enhanced auditory discrimination, but the relative roles of pitch and time in music and speech are unclear. Moreover, it is unclear whether pitch and time processing are correlated across individuals and how they may be affected by attention. This study aimed to examine pitch and time processing in speech and tone sequences, taking musical training and attention into account. Method: Musicians (16) and nonmusicians (16) were asked to detect pitch or timing changes in speech and tone sequences and make a binary response. In some conditions, the participants were focused on 1 aspect of the stimulus (directed attention), and in others, they had to pay attention to all aspects at once (divided attention). Results: As expected, musicians performed better overall. Performance scores on pitch and time tasks were correlated, as were performance scores for speech and tonal stimuli, but most markedly in musicians. All participants performed better on the directed versus divided attention task, but again, musicians performed better than nonmusicians. Conclusion: In general, this experiment shows that individuals with a better sense of pitch discrimination also have a better sense of timing discrimination in the auditory domain. In addition, although musicians perform better overall, these results do not support the idea that musicians have an added advantage for divided attention tasks. These findings serve to better understand how musical training and attention affect pitch and time processing in the context of speech and tones and may have applications in special populations. Supplemental Material: https://doi.org/10.23641/asha.5895997.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".