Opera trainees’ cognitive functioning is associated with physiological stress during performance
Bibliographic record
Abstract
In an opera performance, singers must perform difficult musical repertoire at a high level while dealing with the stress of standing before a large audience. Previous literature suggests that individuals with better cognitive functions experience less stress. During a music performance such functions, especially attention, memory, and executive function, are in high demand, suggesting that cognitive functions may play a role in music performance. This study used physiological and cognitive measures to examine this phenomenon in opera performance. Cardiac activity data were collected from 24 opera trainees during a resting-state period before and during a real-life performance. Heart-rate variability (HRV) was used as an indicator of physiological stress, such that higher HRV indicates lower stress. Standardized neuropsychological tests were used to measure attention (IVA-2), memory (CVLT-3, WMS-IV), and executive function (Trail Making Test). Results showed cognitive function- and state-specific relationships between HRV and cognitive function: HRV during the resting state had a positive correlation with attention, while HRV during a performance had a positive correlation with executive function. These results suggest that greater cognitive function is related to lower stress during opera performance. The findings of this study provide initial evidence for a relationship between cognitive functions and music performance stress in opera trainees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".