Music listening does not inoculate the stress response in young and older adults.
Bibliographic record
Abstract
Although the acute stress response is adaptive for survival, frequent and intermittent activation of the acute stress response may have negative health consequences over time. As such, the development of effective low-cost and noninvasive stress reduction interventions is important. Music listening is shown to promote faster physiological recovery following acute stress. However, there is a paucity of research examining the potential inoculation effect of music on stress reactivity and potential modifiers that may influence this effect such as music selection. Hence, the current study examined the potential inoculation effect of music on acute stress reactivity, as measured by a comprehensive set of stress indices. It was hypothesized that listening to music prior to acute stress exposure would decrease stress reactivity compared with white noise (WN), and that self-selected music would serve as a stronger inoculator than researcher-selected music. Participants (N = 109) were randomly assigned to either researcher-selected music (n = 36), self-selected music (n = 38), or a WN group (n = 35) and listened to either music or WN prior to undergoing the Trier Social Stress Test, a standardized psychosocial stress protocol. Outcome indices of stress included skin conductance, heart rate, salivary cortisol, and self-report affect. Analyses failed to show a significant inoculation effect of music on the stress response. Exploratory analyses did not reveal differential sex or age effects in stress inoculation. This study contributes to the growing literature on the neuroscience of music and presents a new line of questions that require further investigation.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".