Investigating the Effect of Music on the Physiological Response in Anticipation and Jump Scares
Bibliographic record
Abstract
Background: Anticipation and jump scares are both powerful modulators of blood pressure and heart rate. Existing literature has shown that listening to anticipatory music can raise blood pressure and heart rate, while calming music can have the opposite effects. This study investigates the effect music has on the physiological anticipation and jump scare responses. Methods: Participants were allocated to one of two groups: one listened to “Anticipation” by David Fesliyan and one listened to “Weightless” by Marconi Union. All participants were told they would be scared. They then completed two different trials while listening to their assigned music: the “anticipation” trial, where there was no jump scare, and the “jump scare” trial, where a jump scare was conducted. Participants reported Subjective Units of Distress Scale (SUDS) scores pre- and post- experiment. Results: Calming music lowered rate-pressure product during the jump scare. The type of music listened had no observable effect on the physiological anticipation response. Participants who listened to the anticipatory music reported feeling more scared than participants listening to the calming music. Thus, there may be discordance, where the participant felt less subjective fear when listening to calming music, but this was not reflected in their physiological response. High responders did not show a lower RPP during the jump scare when listening to calming music. Conclusion: Calming music was able to mediate the jump scare response. Participants felt less scared when listening to calming music. These findings have possible applications in stress-management strategies.
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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 | 0.001 |
| 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".