Constraints on the rapid interpretation of cast shadows
Bibliographic record
Abstract
Visual search experiments (Rensink & Cavanagh, 1993) have shown that rapid (preattentive) vision can interpret small regions of an image as cast shadows, provided that these regions are dark and lighting is assumed to be from above. Several experiments are presented that extend these results, mapping out the constraints used by this process in regards to color, texture, and the item casting the shadow. Displays consisted of a set of vertically-oriented rectangles, with each rectangle having an attached region that could correspond to a cast shadow. Observers were asked to search for a target with a distinctive orientation to its attached region. In agreement with earlier work, when these regions were black and attached to the bottom of the rectangles (so that items corresponded to shadowed posts lit from above), search was slower than when the displays were rotated upside down (corresponding to lighting from below). This difference was not found when the attached regions were white, indicating that shadow interpretation did not occur for this condition. Interpretation also did not occur when a dark region was outlined by a darker or lighter line, or had a dot along its boundary. It also did not occur when a texture was restricted to the region. However, results on blue and red regions and region outlines showed little evidence for purely chromatic constraints. Interpretation occurred when the item casting the shadow was an outlined triangle and the attached region corresponded to a shadow cast by a rectangle, showing that geometrical constraints are not strong. But it did not occur when a gap was placed in the outline of a shadow caster, indicating that the process distinguishes between surface and line elements, with only the former considered capable of creating a shadow.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".