The Pinna -Brelstaff Illusion is not optimal under self-motion conditions
Bibliographic record
Abstract
Purpose: The Pinna-Brelstaff illusion (Pinna & Brelstaff, 2000, Vision Research) consists of two concentric rings of micropatterns that appear to counter-rotate when the observer moves towards and away from the image. There have been several anecdotal reports that the illusion is stronger when the retinal motion (expansion and contraction of the stimulus) is produced by self-motion rather than by expansion and contraction of the stimulus on the computer screen. Method: Our displays consisted of two concentric rings of Gabor patches. Each micropattern was oriented ±? degrees from the line that connects it to the centre of the display (Gurnsey et al., 2002, Perception). Nine displays were created with θ ranging from 0 to 90 degrees. Subjects first made relative salience judgements (for all possible pairs of θ) under both self-motion and screen-motion conditions. Subjects then judged which of two identical retinal motions—one produced under conditions of screen motion and another under conditions of self motion—produced the stronger illusion. Results: The relative salience judgements made under conditions of screen- and self-motion produced identical dependence on θ, with peak salience occurring at approximately θ = 31. For all values of θ screen motion produced the more salient illusion and in several cases the difference was statistically different from chance. Conclusions: Contrary to anecdotal reports the Pinna-Brelstaff illusion is not strongest under conditions of self-motion; if anything, the opposite is true. Therefore, there is no need to consider a role for extraretinal inputs in determining the strength of the illusion. It should be noted however that the percepts differ under conditions of self motion and screen motion. Under conditions of self motion there is size constancy but not under conditions of screen motion. Nevertheless, the extra retinal contributions leading to size constancy do not affect the strength of the 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 | 0.001 | 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 | 0.001 |
| 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".