The Neural Impact of a Brief Mindfulness and Improvisational Singing Practice on Music Performance Anxiety
Bibliographic record
Abstract
Music Performance Anxiety (MPA) negatively impacts musicians’ personal lives, professional functioning, and performance quality, and is most prevalent in young adulthood. This mixed methods study investigated whether two weeks of a novel intervention called Environmental Vocal Exploration (EVE; Oddy, 2022), combining mindfulness and vocal improvisation, could alter neural activity associated with MPA in young musicians. The EVE intervention was compared with 1) a traditional two-week mindfulness program and 2) no intervention. Pre- and post-intervention, 27 musicians aged 18–28 (n = 7: EVE, n = 10: mindfulness, n = 10: no intervention) completed an anxietyprovoking task and a letter N-back working memory task during fMRI. A thematic analysis was also conducted on discussion transcripts from EVE sessions. During the anxietyprovoking task, blood flow in emotional processing and attention-related brain areas (e.g., limbic and salience network areas) increased from pre- to post-EVE, consistent with themes (e.g., present-moment awareness). During the post-intervention N-back, EVE participants showed less activity than the other groups in cognitive processing areas (e.g., middle cingulate gyrus), indicating enhanced neural efficiency. Both interventions may impact neural activation linked to MPA, through increased attentional and emotional regulation. Vocal improvisation in a mindfulness intervention seemed to augment such effects. Results support further study of EVE’s use for MPA. Acknowledgements The authors would like to acknowledge the Brain Imaging Centre, including Rahim Ismaili, Katie Dinelle, and Reggie Taylor, for their support.
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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 | 0.000 |
| 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".