Auditory global-local processing: Effects of attention and musical experience
Bibliographic record
Abstract
In vision, global (whole) features are typically processed before local (detail) features ("global precedence effect"). However, the distinction between global and local processing is less clear in the auditory domain. The aims of the present study were to investigate: (i) the effects of directed versus divided attention, and (ii) the effect musical training on auditory global-local processing in 16 adult musicians and 16 non-musicians. Participants were presented with short nine-tone melodies, each comprised of three triplet sequences (three-tone units). In a "directed attention" task, participants were asked to focus on either the global or local pitch pattern and had to determine if the pitch pattern went up or down. In a "divided attention" task, participants judged whether the target pattern (up or down) was present or absent. Overall, global structure was perceived faster and more accurately than local structure. The global precedence effect was observed regardless of whether attention was directed to a specific level or divided between levels. Musicians performed more accurately than non-musicians overall, but non-musicians showed a more pronounced global advantage. This study provides evidence for an auditory global precedence effect across attention tasks, and for differences in auditory global-local processing associated with musical experience.
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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 | 0.002 |
| 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) | 0.003 | 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".