Keep Calm and Pump Up the Jams: How Musical Mood and Arousal Affect Visual Attention
Bibliographic record
Abstract
Music is a prevalent part of everyday life and there has been a great deal of interest in the possibility that music facilitates cognition, including memory. Listening to background music has a modulatory effect on internal mood and arousal states, putting the listeners at the optimal levels necessary to enhance memory performance. However, there has been little research on how music-induced mood and arousal influence other aspects of cognition, in particular attention. The aim of the current study was to examine the effect of background music on visual attention. Participants rated an assortment of music clips on mood and arousal levels. The clips that participants rated most positive or negative in mood and highest or lowest in arousal were used during an adaptation of the Posner cueing task ( Posner, 1980 ). This visual attention task was either performed in silence or while listening to background music. A significant interaction between mood and arousal was observed. Participants were fastest when listening to high arousal positive music and slowest when listening to high arousal negative music. Intermediate performance occurred for low arousal negative and low arousal positive music. Thus, changes in music-induced mood and arousal can indeed alter reaction times, with opposite effects observed for high arousal music based on whether it is perceived as positive or negative in mood. However, there is no evidence that musical mood and arousal affect attention because mood and arousal levels do not alter the effect of congruency on either reaction times or accuracy. Thus, although reaction times are faster in the presence of high arousal positive music, this appears unrelated to effects on attention.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".