The Effects of Viewing Angle on the Acquisition, Retention and Recognition of a Complex Dance Sequence
Bibliographic record
Abstract
The benefits of observing a model when acquiring a new motor skill are well known, however, there is little research on the influence of viewing angle of the model. The purpose of the present experiment was to assess whether a looking-glass (face on) or subjective (facing away) viewing angle would result in different acquisition and retention levels when learning a complex Zumba dance sequence. Greater cognitive effort was expected during the looking-glass condition, consequently resulting in slower acquisition but greater physical performance scores and error recognition/identification. Thirty females were evenly divided into the looking-glass or subjective group and began with the pre-test phase to assess degrees of motivation, self-efficacy, and physical performance. Participants were then lead through six acquisition dances, within which they performed the to-be-learned sequence 18 times. An assessment of cognitive effort followed, then post-test performances and error recognition/identification scores were obtained to conclude the study. While both the looking-glass and subjective conditions demonstrated equal rates of acquisition (p>.05), the looking-glass group performed significantly fewer errors during the post-test (p.05). Based on the results of this study, the looking-glass viewing angle appears to result in better learning of a dance sequence, but cannot be explained by cognitive effort.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".