Perceived size during visually simulated self-motion
Bibliographic record
Abstract
INTRODUCTION The perceived distance to objects in the environment needs to be updated during self-motion. Such updating needs to be overridden if the object moves with the observer (such as when reading a phone while walking). Errors in updating could lead to errors in perceived distance and, because of size/distance invariance, to errors in perceived size. To look for such errors, we measured the perceived size of an object that moved with the observer during visually simulated self-motion. METHODS Participants judged whether a vertical rod presented on the ground plane in a virtual-reality-simulated scene at a fixed distance of 2-10m, was longer or shorter than a physical rod (45cm) that they held in their hands either vertically or horizontally. Observers were either stationary or in the presence of optic flow compatible with moving at 1m/s or 10m/s forwards or backwards. Viewing was monoscopic or stereoscopic. Responses were fitted with a logistic to determine the PSE. RESULTS The rod generally needed to be larger than the physical rod to be judged as equal to its size. Errors were smaller when viewing monoscopically compared to stereoscopically (+16%). The orientation of the reference rod influenced size judgements, with larger errors when held horizontally (+16%) compare to when held vertically (+6%). However, there were no significant differences observed in the errors in perceived rod size due to optic flow. CONCLUSION We interpret the changes in the perceived size as resulting from an error in perceived distance. Thus, we confirm the well-known observation that perceived distances are compressed in a virtual environment. However, this compression effect disappeared with monoscopic viewing, despite fewer cues to distance. Our ability to update the distance of an object moving with us appears to be robust during forward and backward self-motion. Meeting abstract presented at VSS 2018
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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 | 0.003 |
| 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) | 0.003 | 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".