Influence of movement expertise on a virtual point-to-origin task
Bibliographic record
Abstract
There is increasing evidence of individual differences in spatial cognitive abilities and strategies, especially for simulated locomotion such as virtual realities. For example, Klatzky and colleagues observed two distinct response patterns in a "point-to-origin" task where participants pointed back to the origin of locomotion after a simulated 2-segment excursion. "Turners" responded as if succeeding to update their heading, whereas "non-turners" responded as if failing to update their heading - but why? Here, we investigated if one's real-world movement and movement analysis expertise (i.e., dancers versus Laban Movement Analysts) might affect one's virtual orientation behaviour. Using a virtual point-to-origin task, data showed that participants (N=39) with more extensive movement analysis expertise tended to be turners, and thus incorporate visually presented turns correctly. Conversely, dance students without Laban Movement Analysis expertise tended to be non-turners or used a mixed strategy. This suggests that reflecting about self-motion might be more conducive than movement experience, primarily dance, alone for enabling correct updating of simulated heading changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".