Biases in Perceived Object Speed in Depth During Visual Self-Motion
Bibliographic record
Abstract
During sideways movement, optic flow parsing - in which an object’s speed is extracted from a scene viewed by a moving observer - has been shown to be incomplete, resulting in an overestimation of object speed when target and observer move in opposite directions (Jörges & Harris, 2022, AP&P 84: 25-46). Here we assess the efficiency of optic flow parsing for an object moving in depth while the observer is also moving towards or away from the object. Participants were immersed in a 3D virtual environment and asked to compare the speed of a sphere moving towards or away from them at 2, 3, and 10m/s relative to a ball moving sideways either while they were stationary or during visually simulated self-motion (either forwards or backwards at 6 or 10 m/s) evoking a range of retinal speeds. The speed of the sideways-moving ball was adjusted using an adaptive staircase to match the perceived speed of the sphere. Overall, flow parsing was incomplete. When the observer and sphere moved in opposite directions, the perceived speed of the sphere was greater than when the observer was static. Results were mixed when the observer and the sphere were moving in the same direction. The perceived direction of the sphere’s movement depended on its retinal motion. We conclude that movement-in-depth flow parsing is incomplete. Our results are relevant to perceptual processing in various real-world settings, such as driving or crossing the road.
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 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 | 0.001 | 0.007 |
| 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".