Eye movements, attention, and expert knowledge in the observation of Bharatanatyam dance
Bibliographic record
Abstract
Previous research indicates that dance expertise affects eye-movement behaviour-dance experts tend to have faster saccades and more tightly clustered fixations than novices when observing dance, suggesting that experts are able to predict movements and process choreographic information more quickly. Relating to this, the present study aimed to explore (1) the effects of expertise on eye movements (as a proxy for attentional focus and the existence of movement-dance schemas) in Indian Bharatanatyam dance, and (2) narrative dance, which is an important component of Bharatanatyam. Fixation durations, dwell times, and fixation-position dispersions were recorded for novices and experts in Bharatanatyam (N = 28) while they observed videos of narrative and non-narrative Bharatanatyam dance. Consistent with previous research, experts had shorter fixation durations and more tightly clustered fixations than novices. Tighter clustering of fixations was also found for narrative dance versus non-narrative. Our results are discussed in relation to previous dance and eye-tracking research.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".