Correlation between perceived size and depth changes in the Dynamic Ebbinghaus illusion
Bibliographic record
Abstract
In the Ebbinghaus illusion, a central disk surrounded by smaller disks appears larger than the same size disk surrounded by larger disks. This illusion surprisingly doubles in magnitude when the stimulus translates while changing continuously (Dynamic Ebbinghaus illusion: Mruczek et al., 2015). Here we test whether the looming motion of the Dynamic Ebbinghaus illusion generates an impression of changing depth that would increase the standard size contrast effect by adding a size-depth scaling. To examine this, we compared the magnitudes of the illusion and perceived depth across three versions of the Ebbinghaus stimulus: a static version (the classic Ebbinghaus illusion), the static version superimposed on a corridor background, and the dynamic version. Participants first nulled any size difference in the central test disk to measure the illusion strength in the three different versions. Participants then made a depth judgment of the whole stimulus comparing the left hand location to the right hand location with the central disk corrected to appear of equal size throughout (using the results of the first experiment). The depth was reported by adjusting the vertical separation of two markers to match the perceived depth as if viewed top-down. There was a positive correlation between the magnitudes of the illusion and the perceived depth. These results suggest that perceived depth, whether from the corridor illusion or the dynamic version adds an additional size change to the effect of the classic, static Ebbinghaus 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 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".