Stereopsis depends on a matched interocular mean luminance
Bibliographic record
Abstract
Stereopsis depends on matched inputs from the two eyes. The role of contrast and spatial frequency have been well studied but we know little about the effects of a mismatch in mean luminance between the two eyes. If stereo depends on a matched luminance between the two eyes there are three possible reasons why. First, even though the physical contrast is unaltered, the neural contrast sensitivity will eventually change and this could result in reduced stereo via its contrast dependence. Second, mean luminance is known to be associated with a slower visual response and this could reduce stereo due to temporal asynchrony. Thirdly, optimal stereo may require a matched interocular mean luminance per se. We thus tested the effects of different neutral density (ND) filters in a disparity detection task using a spatially filtered and unfiltered fractal noise stimulus. We first determined whether the reduction in stereo with mean luminance reduction in one eye was spatial scale dependent. We then determined whether any reduction in stereo performance could be accounted by the expected luminance-dependent temporal asynchrony. The results suggest that stereo does depend on a match mean luminance in the two eyes, it is scale dependent with greater reduction occurring at the lowest scale and that while temporal asynchrony is a factor resulting from reducing the mean luminance, it is not the sole cause of the reduced stereo performance. We conclude that there is a mean luminance per se constraint to stereo matching. Supported by an NSERC grant (# 46528) to RFH Meeting abstract presented at VSS 2013
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".