Creative Flow and Physiologic States in Dancers During Performance
Bibliographic record
Abstract
= 60) participated in this ambulatory psychophysiology study that investigated performance flow and heart rate and autonomic nervous system (ANS) function during three time periods: baseline rest, performance, and post-performance rest. To gather these results, the psychophysiology laboratory traveled to the concert hall to collect data on dancers. The self-report Flow State Scale (FSS) measured global flow, challenge-skill balance, sense of control, and autotelic experiences; it addresses important features of the creative experience of performing artists. These data were collected immediately following the performance. The flow measures were compared with physiologic responses to performance [heart rate, pre-ejection period (PEP), root mean square differences of successive R-R (heartbeat) intervals (RMSSD), cardiac autonomic balance, and cardiac autonomic regulation]. The regression analyses indicated that greater sympathetic nervous system (SNS) activation with performance (PEP change from base to performance) explained 8.8% of the variance in sense of control, whereas less cardiac autonomic regulation explained 13.8% of the variance in autotelic experiences. The sample was then divided into high and low flow groupings and four autonomic groups. During performance, the high autotelic group and high sense of control group had a higher distribution of dancers with co-inhibition of both ANS branches than had the low autotelic and sense of control groups who employed more co-activation of both ANS branches (chi-square analyses). These novel findings add to the growing information about the interaction of both branches of the ANS during creative performance flow states.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".