Clouds are not normal occluders, and other oddities: More interactions between textures and lightness illusions
Bibliographic record
Abstract
Identical textured disks can appear white or black depending on the luminance properties of the surrounding textured region (B. L. Anderson & J. Winawer, 2005, 2008). This occurs when the stimulus is perceptually segmented in three layers: (1) a uniform foreground disk, (2) a uniform background surface, and (3) a cloud-like layer that covers parts of the foreground and background regions. However, local occlusion cues fail to predict the pattern of data observed, suggesting that in some cases a different strategy may be adopted depending on texture characteristics (F. J. A. M. Poirier, 2009). Here, we produced a variety of stimuli using three different textures and several luminance configurations (including the White and inverse White configurations and the Anderson-Winawer illusion), for which participants reported the perceived characteristics of the central disk (e.g., lightness, transparency, whether the disk was textured). The results show several interactions between textures and luminance configurations, which we account for using mathematical models of previously documented strategies. We show how the strategies chosen depend on an interaction between texture properties and luminance configuration.
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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 | 0.005 |
| 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 | 0.001 |
| 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".