The effect of physical and virtual rotations of a 3D object on spatial perception
Bibliographic record
Abstract
We question whether a virtual reality (VR) environment produces the same spatial perception as natural world. In this study, as a first attempt to answer the above question, we examined the perceived rotation of an object (a building) in both virtual and natural environments on spatial ability of 30 young males, while navigating in a virtual environment. We calculated the number of errors they made in finding a destination in a virtual reality navigational environment. The subjects performed three sets of 4 trials of finding a target room in a virtual building with no landmarks. At the beginning of each trial the target room was shown to the subject by rotation of the building from outside perspective. The building rotation was achieved in three conditions: 1) in virtual environment, 2) in real environment by rotating an identical but scaled physical building, and 3) by walking around the physical model. Each subject performed 4 trials of virtual navigation under each of the above conditions. In each trial, the traversed distance and the visited rooms were recorded by the program. One-way analysis of variance (ANOVA) was employed to find any statistical difference between the particpants' errors in three conditions. Overall, no statistically significant differences were found between the error scores in any of the three conditions. The results and their implications are discussed.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".