Evidence for binocular differencing and summing channels for chromatic stimuli
Bibliographic record
Abstract
Mounting evidence suggests that in binocular vision there exist channels that sum (S+) and difference (S-) the luminance contrast signals from the two eyes. While there is strong evidence from studies of chromatic binocular summation for chromatic S+ channels, there is little or no direct evidence for chromatic S- channels. Here we test for the presence of chromatic S- channels using a two-interval forced-choice surround masking paradigm aimed at selectively attenuating S+ and S- channel signals. Interocularly correlated (C) gratings are believed to be detected by S+ channels and interocularly anticorrelated (A) gratings by S- channels, so C and A surround masks would be expected to selectively attenuate C and A test stimuli respectively. Stimuli were horizontally oriented 0.5 cpd gratings. The test stimulus was contained in a hard-edged 2 deg diameter circular window separated by a 0.25 deg gap from the 6.5 deg diameter circular mask grating. Stimuli were defined along the three cardinal axes of color space to produce what may be nominally termed red-green (RG), blue-yellow (BY) and luminance (LUM) stimuli. Masks and test stimuli were always of the same color type. Measurements consisted of binocular C and A detection thresholds and monocular detection thresholds, allowing us to determine the degree of binocular summation in the C and A stimuli. Results showed that all three color types exhibited the selective masking associated with the detection of stimuli in the presence of S+ and S- channels. For all three color types, the differential effect of the two types of masks was greater for the A compared to C stimuli. Binocular summation for both the A and C stimuli was also markedly mask selective. These findings offer strong support for the existence of both S- and S+ channels in the chromatic domain.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".