Temporal deployment of attention in musicians: Evidence from an attentional blink paradigm
Bibliographic record
Abstract
The generalization of music training to unrelated nonmusical domains is well established and may reflect musicians' superior ability to regulate attention. We investigated the temporal deployment of attention in musicians and nonmusicians using scalp-recording of event-related potentials in an attentional blink (AB) paradigm. Participants listened to rapid sequences of stimuli and identified target and probe sounds. The AB was defined as a probe identification deficit when the probe closely follows the target. The sequence of stimuli was preceded by a neutral or informative cue about the probe position within the sequence. Musicians outperformed nonmusicians in identifying the target and probe. In both groups, cueing improved target and probe identification and reduced the AB. The informative cue elicited a sustained potential, which was more prominent in musicians than nonmusicians over left temporal areas and yielded a larger N1 amplitude elicited by the target. The N1 was larger in musicians than nonmusicians, and its amplitude over the left frontocentral cortex of musicians correlated with accuracy. Together, these results reveal musicians' superior ability to regulate attention, allowing them to prepare for incoming stimuli, thereby improving sound object identification. This capacity to manage attentional resources to optimize task performance may generalize to nonmusical activities.
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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) | 0.001 | 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".