Why do LCD screens appear to glow?
Bibliographic record
Abstract
High-resolution LCD screens can depict realistic scenes, but even under restricted viewing conditions (e.g., monocular, stationary) we can usually tell that the surfaces and objects shown are not real. One reason may be that we can tell that the screen emits light instead of simply reflecting incident light. Here we investigated what cues allow observers to determine that a small patch of an LCD screen is light-emitting rather than reflective. We cut a 3 x 3 grid of nine 3.2 cm square apertures in each of 27 black cardboard panels. Behind eight randomly selected apertures on each board we attached patches of gray and off-gray (e.g., beige) paper; we left the ninth aperture empty. The paper patches were picked randomly from twelve samples. On each trial we put a board in front of a light-emitting LCD screen, and the observer judged which aperture contained the screen. In the luminance-match and colour-match conditions, the screen showed a gray region whose luminance or colour (i.e., CIE XYZ coordinates), respectively, were matched to a randomly chosen paper patch. In the texture-match condition the screen showed a colour-calibrated photograph of a randomly chosen paper patch. The three 108-trial conditions were randomly interleaved. All observers (n=5) were well above chance performance in the luminance-match condition (95% correct), two were above chance in the color-match condition (16% correct), and three were above chance in the texture-match condition (30% correct). We conclude that color is an important cue for glow detection, but not the only relevant cue. Further work will explore the role of cast shadows and texture-based lighting direction cues in making LCD screens discriminable from reflective surfaces. Meeting abstract presented at VSS 2017
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".