Anxiety and Optimal Piano Performance: A Pilot Study on the Application of the Individual Zone of Optimal Functioning (IZOF) Model
Bibliographic record
Abstract
Music Performance Anxiety (MPA) is a common problem for musicians. Many musicians struggle with performance anxiety and rely on traditional de-arousal interventions to reduce performance anxiety before public performance. However, research in sports psychology suggests that anxiety reduction may not be the most appropriate strategy for intervention (Chamberlain & Hale, 2007). According to the Individual Zone of Optimal Functioning (IZOF) model proposed by Hanin, an athlete’s performance is successful when his or her pre-competition anxiety is within or near the individual’s optimal zone (Hanin, 2000). Based on the application of the IZOF theory in the context of piano performance, anxiety plays an important role in optimizing performance in music as well. This pilot study identified participants’ IZOFs with the Competitive State Anxiety Inventory (CSAI-2). Support was found for Hanin’s IZOF theory with respect to the SA (somatic anxiety) and SC (self-confidence) dimensions for both of the participating pianists, as well as the CA (cognitive anxiety) dimension of pianist A but not for the CA dimension of pianist B. Piano performances associated with anxiety of an intensity that fell within the IZOF were observed to be significantly better than piano performances associated with anxiety intensity outside the IZOF. All the peak performances were presented within the IZOFs. The study verified that the IZOF model can be applied in MPA management and may help pianists be more aware of in-zone/out-zone states and rethink their attitudes toward performance anxiety. With this pilot study as a foundation, larger scale research can be conducted to clarify the correlation between anxiety and optimal piano performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".