Perceived 3D Shape Toggles Perceived Glow
Bibliographic record
Abstract
Most surfaces reflect light from external sources, but others emit their own light, or glow. Glowing surfaces often signify an important feature in the environment (e.g., heat source or bioluminescent life form), but we know little about how the visual system identifies them. Here, we show that perceived 3D shape is critical for perceived glow. In Experiment 1, we created "dark-means-deep" stimuli by rendering stereoscopic pairs of wavy 3D surfaces under diffuse light (non-directional lighting on a cloudy day). This generated stimuli with bright peaks and dark valleys. We created "bright-means-deep" stimuli by using the same luminance images, but reversing the disparity to get dark peaks and bright valleys. Subjectively, dark-means-deep stimuli appeared evenly lit from the front, whereas bright-means-deep stimuli produced a vivid impression of glow. On a mirror stereoscope, we displayed dark-means-deep and bright-means-deep stimuli side-by-side, and asked observers to choose the one that appeared to glow. Five of six observers consistently identified the bright-means-deep stimuli as glowing. In a follow-up study, we assessed depth percepts for the same observers and stimuli using a depth-probe task ("is the probe on a peak or in a valley?"). Five of six observers performed almost perfectly. The sixth observer (the anomalous observer from above) ignored disparity cues, always judging bright regions as peaks and dark regions as valleys. Thus, this observer was anomalous because disparity cues did not affect their shape percepts. In Experiment 2, we used the same observers, stimuli, and methods, except that we used motion parallax instead of disparity to reveal surface relief. All observers, including the anomalous one, identified bright-means-deep stimuli as glowing, demonstrating that the glow effect was not tied to a particular depth cue. Our results demonstrate that human vision has a sophisticated understanding of lighting geometry, interpreting complex shape-luminance relationships to identify glowing surfaces. Meeting abstract presented at VSS 2016
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".