Investigating the Perception of Travel Distance Using Visual and Non-Visual Self-Motion Cues
Bibliographic record
Abstract
Having an accurate perception of travel distance is essential for navigating and moving through the world. In Chapter 2, I show that transforming visual motion into travel distance differs depending on the speed and direction of optic flow being perceived. Motion at the slower speed was associated with people feeling like they had moved further compared to motion at the faster speeds. In Chapter 3, I use a series of 4 experiments to investigate how various characteristics of a virtual environment affects your perception of travel distance through it. The specific parameters I investigated were: (1) the structure of an environment, and presence and texture of a ground surface, (2) the naturalism and scale of an environment, (3) colour, and (4) the density of a starfield. Results show no effect of the structure of an environment, and the presence of a ground surface (Experiment 1), or between the naturalism of an environment (Experiment 2), or whether the environment has colour or not (Experiment 3). However, I did find a small effect of the texture of the ground surface and the scale of an environment. In Experiment 4, I also show that there may be a ceiling effect with the starfield density needed to accurately estimate travel distance. Together these experiments will have implications for the design of real and virtual environments where perceived motion is important and enable us to further predict our perception of moving through these environments. In Chapter 4, I used a large-field edgeless display to either visually “move” participants while they were (i) physically stationary, (ii) performing a blind walking task on a treadmill, or (iii) visually “moving” while walking on a treadmill. Optic flow simulating forward self-motion was presented either full field, in the central field (central ±20°), or in the far periphery (beyond ±90°). I show that the high sensitivity to optic flow in the far periphery is a general feature of perceptual odometry even when integrating non-visual cues with visual cues.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".