The Effects of Musical Mood and Musical Arousal on Visual Attention
Bibliographic record
Abstract
The presence of music is a visceral part of the human experience and its influence on cognitive function is a growing area of research in psychology. In particular, perceptual properties of music (mood and arousal) have been shown to significantly affect performance. There has been minimal research in the field on the interaction of mood and arousal and their influence on attention, thus the purpose of this study. Fifty undergraduate students currently enrolled at the University of Western Ontario were recruited for this study. Given that music is a highly subjective experience, participants rated an assortment of music clips on their mood and arousal levels. The clips that participants rated highest and lowest on mood (positive and negative) and arousal (low and high) were chosen for use on the Posner cueing task. This visual attention task was either performed in silence or while listening to music clips as per their ratings. Results indicated that musical mood and musical arousal, independently of one another, had no significant effect on visual attention. Rather, a significant interaction between the two perceptual properties was observed. The fastest reaction times were recorded when participants listened to high arousal positive music and the longest reaction times were found when participants listened to high arousal negative music. Intermediate performance occurred when participants listened to low arousal negative music and low arousal positive music. Future studies should investigate whether the combined modulatory effects of musical mood and musical arousal generalize to other attentional paradigms.
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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) | 0.002 | 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".