Pupil dilation to illusory motion in peripheral drift images: Perception versus reality
Bibliographic record
Abstract
Peripheral drift is a specific type of illusory motion that causes observers to perceive motion in a static image. We aimed to determine whether pupil dilation occurs during the perception of illusory motion. In three experiments investigating pupil-size changes to peripheral drift, pupil response differences were observed between symmetric patterns (SPs) that elicited no impression of motion and repeated asymmetric patterns (RAPs) that did. All participants reported the perception of motion in the RAP condition and showed significantly greater pupil dilation to these stimuli as compared with viewing stimuli in the SP condition. As a follow-up, we manipulated the RAP stimuli to reduce and then remove the illusion to determine (a) whether it was the asymmetry per se that induced the pupil dilation and (b) whether the amount of pupil dilation was contingent on the amount of observed illusory motion. Although a reduction in perceived illusory motion did not produce a reduction in pupil dilation, removal of the illusory motion did. Despite previous evidence reporting pupil constriction to the perception of motion, and the positive valence associated with symmetry, these experiments show that pupil dilation occurs during the perception of illusory motion. This is in keeping with previous evidence that pupil dilation is influenced by perceptual factors and not simply light level, and, in particular, shows that illusory motion is physiologically arousing.
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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 | 0.003 |
| 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 | 0.000 | 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".