Dichoptic difference thresholds for uniform color changes applied to natural scenes
Bibliographic record
Abstract
It has recently been shown that the visual system is more sensitive to uniform color and/or luminance changes applied to raw compared to phase-scrambled images of natural scenes (A. Yoonessi & F. A. A. Kingdom, 2008). Here we consider whether the mechanisms responsible for the differential sensitivity operate before or after the point at which the signals from the two eyes are combined. Knowing this should help determine the types of nonlinearities responsible. Thresholds for detecting uniform color transformations applied to raw and phase-scrambled natural scenes were measured under two conditions: monocular, in which the discriminand pairs were placed side by side, and dichoptic, in which they were dichoptically superimposed. Subjects were required to select the pair of images that were transformed from two pairs of images in which the other pair was untransformed. In the dichoptic condition, the transformed image pair was identifiable by its lustrous appearance. In line with our previous findings, thresholds in the monocular condition were higher for the phase-scrambled compared to raw scenes. However in the dichoptic condition there was no significant difference between raw and phase-scrambled thresholds, suggesting that the differential sensitivity was mediated by mechanisms lying beyond the point of binocular combination. It is suggested that cortical neurons sensitive to edges but suppressed by neighboring texture might be responsible for the higher sensitivity to transformations applied to raw compared to phase-scrambled images of natural scenes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".