The Effects of Environmental Structure and Texture on Perceived Travel Distance
Bibliographic record
Abstract
Although virtual reality (VR) and visually induced self-motion have been widely used to investigate the perception of travel distance, the characteristics of these virtual environments varies greatly between studies. Previous research from our lab has found that when visually moving through a structured virtual corridor, people feel they have moved further compared to when moving through a less structured environment (e.g., McManus & Harris, 2021, Bury et al., 2020). There are countless parameters that contribute to the processing of optic flow (Seno et al., 2010; Bubka & Bonato, 2010), although these variables are rarely taken into consideration in peoples’ experimental design. Here we test how the presence and texture of a ground surface effects perceived travel distance in VR. We compared the effectiveness of a structured virtual corridor environment (similar to Redlick et al., 2001) with a less structured “starfield” environment (similar to McManus & Harris, 2021). We also varied whether or not a floor surface was present and if it had a texture. Participants saw a target that then disappeared, whereupon they experienced simulated motion at a constant velocity and indicated when they felt they had reached the target’s previous location. Data were analyzed in terms of gain (perceived travel distance/actual travel distance). Preliminary results (n=7) show significant differences between gains in the different environments. The structured virtual corridor evoked the sensation of moving further (higher gains) than the less structured starfield (lower gains). The type of floor surface did not effect gains, however there was an interaction between the environment type and the type of floor surface. This study will enable us to predict the effect of an environment’s structure on the perception of moving through it, which will have implications for the design of real and virtual environments where perceived motion is important.
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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 | 0.000 | 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".