Music’s Influence on Cardiovascular Parameters During Cognitive Testing
Bibliographic record
Abstract
BackgroundMozart’s music has been found to decrease stress and anxiety by decreasing blood pressure and heart rate in diverse populations. However, the influence on Mozart during cognitive tests remains unclear. Due to its proven anti-stress effects, Mozart’s music may be an effective tool for decreasing cardiovascular parameters during cognitive testing. MethodsThe cognitive test chosen was an untimed pattern-recognition Mensa test. The chosen song was Mozart’s Sonata K448. We included 10 adult participants who met the inclusion criteria. A matched pair study design was incorporated. All participants completed a diagnostic test that assessed relative cardiovascular sensitivities to the acute stress produced by the Mensa test. Participants were matched based on cardiovascular sensitivities, rating of momentary stress, and sex. Pairs were then randomly allocated to take the test again; one took the test while listening to Mozart’s Sonata K448, the other took it in silence. Post-test evaluations were then completed. ResultsIn both groups, the intervention did not induce significant changes in BP or HR, except for a significant decrease in SBP in the no music group (p=0.043). When comparing the difference in relative blood pressure and heart rate changes between the two groups, none were significantly different. There were significant decreases in perceived stress and increases in perceived relaxation in the music group (p=0.024 and p=0.039, respectively). ConclusionOur findings suggest that Mozart’s music does not decrease cardiovascular responses during cognitive testing. Additionally, perceived stress is independent of cardiovascular indicators of stress which has unknown 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 | 0.000 | 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".