Navigating mazes in a virtual environment
Bibliographic record
Abstract
In this research we are concerned with computer interfaces with which subjects navigate through maze simulations which are essentially buildings, with corridors and intersections, such as frequently encountered in computer games and simulations. We wish to determine if virtual reality interfaces introduce a performance enhancement that might be expected for display configurations which mimic natural perceptual experiences. We have experimented primarily with two display conditions for presentation of and navigation through the mazes. Subjects either view the maze on a desktop computer monitor, turning and moving within the maze with the mouse in a way that is similar to the configurations used in most first-person role playing computer games, or they viewed the maze from a standing position with a head-mounted display, being free to direct the view of the maze through body and head movements, and using the depression of a mouse button to effect movement in the direction that they were facing. Head-tracking was required for this latter condition. As expected there are striking individual differences in subjects’ abilities to learn to traverse the mazes. Across a variety of maze configuration parameters which significantly do influence performance, the results indicate that the virtual reality enhancements have no effect subjects' ability to learn the mazes, either as route knowledge or as cognitive maps.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".