Effects of Movement Improvisation and Aerobic Dancing on Motor Creativity and Divergent Thinking
Bibliographic record
Abstract
Abstract Creativity is considered to be an embodied concept, where internal psychological and external behavioral processes are intertwined. Creativity enhancement programs often target the cognitive side of this bi‐dimensionality leaving the impact of motor interventions underexplored. To address this gap in the literature, we tested the effectiveness of two motor programs on motor creativity and divergent thinking (verbal and figural). A total of 92 college students (Mage = 25.36, SD = 2.66) were randomly allocated to a movement improvisation, an aerobic dance, or a control condition. Participants in both motor programs took part in ten 30‐minute classes twice a week over a period of 5 weeks. The findings revealed a significant effect of the motor programs on motor fluency and flexibility. Movement improvisation yielded the greatest effects on those variables, followed by aerobic dancing and control condition. Movement improvisation also impacted significantly more figural originality than the control condition. However, the effects were limited to the motor domain and failed to transfer into other divergent thinking variables. The findings highlighted the contribution of movement programs to creative potential development, and the imperative role of a non‐judgmental environment, where individuals are free to move spontaneously.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".