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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".