The perceived position of a moving object is reset by temporal, not spatial limits
Bibliographic record
Abstract
Abstract When the internal texture of a Gabor patch drifts orthogonally to its physical path, its perceived motion deviates dramatically from its physical path. The local position shifts accumulate to such an extent that a 45° oblique physical path appears to be vertical. However, at some point, a limit is reached and the path resets back to its veridical location, whereupon a new accumulation starts, making the new perceived path segment appear parallel to the pre-reset segment, but offset horizontally from it. Here, we tested whether spontaneous resets of this motion-induced position shift depend on the time or the distance over which position errors accrue, or both. We introduced a temporal gap in the middle of the path that forced the illusory path to reset back to its veridical physical position. This gap-triggered reset allowed us to measure the magnitude of the illusory offset up to that point. We found that perceived offset was less than expected for the angle of illusory drift, indicating that spontaneous resets had occurred prior to the gap-induced reset. The position offset decreased when the pre-gap duration increased but approximately doubled when the path length doubled. This pattern of perceived offsets is best accounted for by spontaneous resets that occur randomly over time at a constant rate, independently of the distance traveled. Our results suggest a temporal, not spatial, limit for the accumulation of position errors that underlies this illusion.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".