Rapid Processing of Cast and Attached Shadows
Bibliographic record
Abstract
We used a visual-search method to investigate the role of shadows in the rapid discrimination of scene properties. Targets and distractors were light or dark 2-D crescents of identical shape and size, on a mid-grey background. From the dark stimuli, illusory 3-D shapes can be created by blurring one arc of the crescent. If the inner arc is blurred, the stimulus is perceived as a curved surface with attached shadow. If the outer arc is blurred, the stimulus is perceived as a flat surface casting a shadow. In a series of five experiments, we used this simple stimulus to map out the shadow properties that the human visual system can rapidly detect and discriminate. To subtract out 2-D image factors, we compared search performance for dark-shadow stimuli with performance for light-shadow stimuli which generally do not elicit strong 3-D percepts. We found that the human visual system is capable of rapid discrimination based upon a number of different shadow properties, including the type of the shadow (cast or attached), the direction of the shadow, and the displacement of the shadow. While it is clear that shadows are not simply discounted in rapid search, it is unclear at this stage whether rapid discrimination is acting upon shadows per se or upon representations of 3-D object shape and position elicited by perceived shadows.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".