Locomotion for navigation in virtual environments: Walking, turning, and joystick modalities compared
Bibliographic record
Abstract
Considerable evidence shows that people have difficulty maintaining orientation in virtual environments. This difficulty is usually attributed to poor idiothetic cues, such as the absence of proprioception. The absence of proprioceptive cues makes a strong argument against the use of a joystick interface for locomotion. The importance of full physical movement for navigation has also recently been confirmed (Ruddle and Lessels, 2006), where subjects performed a navigational task better when they walked freely rather than when they could only physically rotate or only move virtually. Our experiment replicates the experiment of Ruddle and Lessels but under different conditions. Here all conditions are conducted using a head-mounted display, whereas Ruddle and Lessels mixed display types. Our environment contains no environmental cues to geometry, as all landmarks are either randomly placed and oriented, or absent, whereas the Ruddle and Lessels environment included a simulated rectangular room that was always visible. People are sensitive to environmental geometry, but the effect on navigation is an active area of research (Kelly et al., 2008), thus our environment omitted them. In this experiment, subjects (N=12) locomoted through an environment in one of three ways: they walked, they used the joystick to translate while physically rotating their bodies to change orientation, or they used a joystick to both translate and rotate with no physical movement occurring. A within-subjects design found that subjects were marginally better in the walking condition than in other conditions (F(1,11) = 2.88, p=.07). Subjects were significantly slower in the joystick condition than in other conditions (F(1,1)=5.44, p=.01). Subjects traveled significantly less distance in completing the task in the walking condition than in other conditions (F(1,11)=4.28, p=.03). In general, we conclude that walking seems a better method for locomotion in virtual environments than locomoting with a joystick.
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".