Evaluating visual/motor co-location in fish-tank virtual reality
Bibliographic record
Abstract
Virtual reality systems often co-locate the display and input (motor) spaces. Many input devices, such as the mouse, use indirect input mappings, and are disjoint from the display space. A study of visual/motor co-location was conducted to determine if there is any benefit to working directly ¿in¿ a virtual environment. Using a fish-tank VR setup, participants performed a 3D object movement task. This required moving an object from the centre of the environment to target regions, using a tracked pen, in both co-located and disjoint display/input conditions. Results were analyzed in the context of Fitts' Law, which models rapid aimed movements. Ultimately, no significant differences were found between co-located and disjoint conditions. However, when analyzing object movement in specific directions, the co-located condition was somewhat better than the disjoint one. In particular, movement into the scene was faster when the display and input device were co-located rather than disjoint.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".