Cueing Morality: The Effect of High-Pitched Music on Healthy Choice
Bibliographic record
Abstract
Managers often use music as a marketing tool. For example, in advertising, they use music to intensify emotions; in service settings, they use slow music to boost relaxation and classical music to convey sophistication. In this article, the authors posit a novel effect—higher-pitched music can boost healthier choices. Recognizing that many perceptual characteristics of higher pitch (e.g., lighter, elevated) are conceptually associated with morality, they theorize that listening to higher- (vs. lower-) pitched music can cue morality. Furthermore, thoughts about morality can prompt moral self-perceptions and, in turn, thoughts about “good” behaviors, including healthy choices. Thus, listening to higher-pitched music may increase healthier choices. Employing field settings and online studies, the authors find that listening to higher-pitched music increases consumers’ likelihood to choose healthy options (Studies 1, 3, and 5), choose lower-calorie foods (Study 2), and engage in health-boosting activities (Study 4). This effect arises because high pitch raises the salience of morality thoughts (Studies 4 and 5). The article concludes with a discussion of theoretical and managerial implications.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".