Illumination Colour, Texture, and the Appearance of Glow
Bibliographic record
Abstract
Even under restricted viewing conditions (e.g., monocular, stationary) people usually recognize that an LCD screen emits light instead of reflecting incident light. In previous experiments, we found that colour and texture were driving cues for glow detection with LCD screens. When a translucent, textured paper sample was placed in front of a computer screen, and the CIE xy chromaticity coordinates of the screen were matched to paper samples, participants were unable to differentiate between small patches of an LCD screen and real paper. Here, we hypothesize that the same realism can be achieved by matching the CIE xy chromaticity coordinates of the environment's lighting to the LCD monitor's white point. In a 9AFC task, observers viewed a 3x3 grid of nine 3.2 cm square apertures. Through one randomly chosen aperture, observers viewed a sample of translucent paper on an LCD screen, and through the other eight apertures, they viewed samples of opaque paper. The observer judged which aperture was light-emitting rather than reflective. Conditions 1 and 2 took place under beige ambient lights (CIE x=0.39; y=0.38). In these condition, the (1) luminance or (2) colour (CIE XYZ) of the translucent paper was matched to a randomly chosen paper sample. In condition 3, the lighting in the room was matched to the computer's white point (x=0.31, y=0.32) and the screen showed luminance-calibrated patches. Observers were significantly better at identifying the light-emitting patch in condition 1 than in conditions 2 and 3, but performance in the latter two was still above chance. We conclude that neither color matching screen display under normal room lighting or matching the chromaticity of the environment's lighting to an LCD screen is sufficient to eliminate cues to glow. Meeting abstract presented at VSS 2018
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".