Imagery-Based Interventions for Music Performance Anxiety: An Integrative Review
Bibliographic record
Abstract
Many musicians experience debilitating music performance anxiety (MPA). Outside music performance, imagery-based interventions have been incorporated into treatment protocols to help individuals, including athletes and those with social anxiety, manage heightened levels of anxiety in order to excel in performance-based domains. Despite the frequent use of mental imagery in MPA interventions and its importance as a mental rehearsal technique for musicians, no existing reviews have examined the literature on imagery-based interventions for MPA. The primary aim of this review was to analyze the existing MPA literature in order to summarize what is known about the efficacy and mechanisms of pre-performance mental imagery exercises. A literature search yielded eight studies that used imagery-based interventions for MPA, in both student and professional musicians, which included three dissertations and five peer-reviewed journal articles. In extant MPA treatment research, pre-performance imagery is often used in conjunction with other techniques in order to alleviate anxiety. Arousal imagery refers to imagining one's state of arousal during performance and has been incorporated into MPA interventions in various ways that guide musicians to anticipate the heightened arousal that accompanies performance, predominantly through imagery-based relaxation techniques. However, methodological limitations make it impossible to determine whether imagery is itself an active ingredient of treatment that underlies symptom changes, or whether relaxation imagery is the most effective use of pre-performance imagery for all musicians. There is much need for future well-controlled studies to examine whether and how imagery affects MPA independent of the other therapy components and techniques with which it is commonly combined.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".