Motor Simulation without Motor Expertise: Enhanced Corticospinal Excitability in Visually Experienced Dance Spectators
Bibliographic record
Abstract
The human "mirror-system" is suggested to play a crucial role in action observation and execution, and is characterized by activity in the premotor and parietal cortices during the passive observation of movements. The previous motor experience of the observer has been shown to enhance the activity in this network. Yet visual experience could also have a determinant influence when watching more complex actions, as in dance performances. Here we tested the impact visual experience has on motor simulation when watching dance, by measuring changes in corticospinal excitability. We also tested the effects of empathic abilities. To fully match the participants' long-term visual experience with the present experimental setting, we used three live solo dance performances: ballet, Indian dance, and non-dance. Participants were either frequent dance spectators of ballet or Indian dance, or "novices" who never watched dance. None of the spectators had been physically trained in these dance styles. Transcranial magnetic stimulation was used to measure corticospinal excitability by means of motor-evoked potentials (MEPs) in both the hand and the arm, because the hand is specifically used in Indian dance and the arm is frequently engaged in ballet dance movements. We observed that frequent ballet spectators showed larger MEP amplitudes in the arm muscles when watching ballet compared to when they watched other performances. We also found that the higher Indian dance spectators scored on the fantasy subscale of the Interpersonal Reactivity Index, the larger their MEPs were in the arms when watching Indian dance. Our results show that even without physical training, corticospinal excitability can be enhanced as a function of either visual experience or the tendency to imaginatively transpose oneself into fictional characters. We suggest that spectators covertly simulate the movements for which they have acquired visual experience, and that empathic abilities heighten motor resonance during dance observation.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".