Barber-pole illusion: The contribution of long edges in motion perception
Bibliographic record
Abstract
The perceived motion direction of objects considerably relies on the motion signals along the edges of the object as demonstrated, for instance, in the barber-pole illusion. The barber-pole illusion is composed of drifting bars viewed through a rectangular aperture in which the short and long edges are oblique (45 deg) to the bars. In this illusion, motion tends to be perceived in the direction close to the long edges of the rectangular aperture instead of perpendicular to the bars (Fourier motion). The present study investigated how the motion signals along the edges in the barber-pole illusion are integrated into a global motion percept. Nine participants were asked to report the perceived motion direction when viewing the barber-pole illusion with their peripheral vision (25 degrees of eccentricity). The length of the rectangular aperture was systematically varied so that the long edges were 1, 2, 3 or 4 times longer than the short edges (1 creating a square-shaped aperture). The motion direction was perceived 1.4±0.8, 27.7±2.5, 35.2±2.1 and 38.4±1.9 degrees (mean±SE) from the Fourier motion (-45 and 45 degrees correspond to the motion direction along the short and long edges, respectively). The perceived motion direction could not be explained by a simple averaging of motion along the edges as this would be equivalent to attributing weights to edges that are proportional to their length. More specifically, this simple averaging would predict perceived motion directions of 0, 15, 22.5 and 27 degrees, respectively. On the other hand, attributing weights proportional to the square of the edge lengths would predict perceived motion directions of 0, 27, 36 and 39.7 degrees, respectively, which closely fits the data. We conclude that the visual system does not simply average the motion along the edges, it rather attributes considerably more weight to motion along the long edges.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".