Motion Prediction is Biased by Visually Simulated Self-Motion
Bibliographic record
Abstract
We have recently shown that the perceived speed of a moving object is systematically biased when the observer experiences visually induced self-motion while judging its speed. Here, we investigated how visual self-motion affects our ability to predict the future motion of a moving object. In accordance with previous results showing an overestimation of perceived speed when observer and object move in opposite directions [1], we expected participants to underestimate the time it took an object to move a certain distance. To this end, we immersed participants in a virtual 3D environment. They were shown a large ball flying laterally (at 4, 5 or 6m/s, 10m in front of the observer) towards an earth-stationary target rectangle. The ball disappeared after 0.5s and participants had to indicate by button press when they thought it would have reached the target rectangle. While the ball was visible, participants were exposed to visually simulated lateral self-motion (at a mean speed of 3.6m/s) in the same or opposite direction as the moving ball, or they could be static. We found that participants generally underestimated how long it would take the ball to reach the target rectangle when they moved in the direction opposite to that of the ball and thus pressed the button too early, in line with our prediction. We further discuss results for self-motion in the same direction as the object and differences in precision between the self-motion conditions. These results suggest that biases in speed perception can translate to predictions about the future trajectory of the object, an interaction that should be taken into account when establish models of interception for dynamic observers in complex environments. [1] Jörges & Harris (2021). Atten Percept Psychophys DOI: 10.3758/s13414-021-02411-0
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 | 0.018 |
| 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".