Luminance and color inputs to mid-level and high-level vision
Bibliographic record
Abstract
We investigated the interdependence of activity within the luminance (L + M) and opponent chromatic (L - M and S - [L + M]) postreceptoral mechanisms in mid-level and high-level vision. Mid-level processes extract contours and perform figure-background organization whereas high-level processes depend on additional semantic input, such as object knowledge. We collected mid-level (good/poor continuation) and high-level (object/nonobject) two-alternative forced-choice discrimination threshold data over a range of conditions that isolate mechanisms or simultaneously stimulate them. The L - M mechanism drove discrimination in the presence of very low luminance inputs. Contrast-dependent interactions between the luminance and L - M as well as combined L - M and S - (L + M) inputs were also found, but S - (L + M) signals, on their own, did not interact with luminance. Mean mid-level and high-level thresholds were related, with luminance providing inputs capable of sustaining performance over a broader, linearly corresponding range of contrasts when compared to L - M signals. The observed interactions are likely to be driven by L - M signals and relatively low luminance signals (approximately 0.05-0.09 L + M contrast) facilitating each other. The results are consistent with previous findings on low-level interactions between chromatic and luminance signals and demonstrate that functional interdependence between the geniculate mechanisms extends to the highest stages of the visual hierarchy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".