Effects of noise and music on human and task performance: A systematic review
Bibliographic record
Abstract
The purpose of the present paper was to review the literature to develop an understanding of the effects of noise and music on human performance. The second purpose was to study the effects of music on a commonly performed task that is frequently accompanied by background music: driving. Background noise not only affects public health, but it also negatively affects human performance in such tasks as comprehension, attention, and vigilance. However, some studies have indicated that noise exposure may not affect simple vigilance. Despite music's distinct difference from noise it too affects human performance negatively and positively. The results are inconclusive on the effects of music and task performance. More specifically, the effects of music on driving performance are quite similar to that of noise on task performance. Music seems to alleviate driver stress and mild aggression while at times facilitating performance. However, during other conditions of music, driving performance is impaired. Different aspects of sound (i.e. volume, type, tempo) impact human performance differently. It is still unknown which aspect (music or noise) affects task performance to a greater degree.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".