Visual Search in the Real World: Color Vision Deficiency Affects Peripheral Guidance, but Leaves Foveal Verification Largely Unaffected
Bibliographic record
Abstract
BACKGROUND: People with color vision deficiencies report numerous limitations in daily life, restricting, for example, their access to some professions. However, they use basic color terms systematically and in a similar manner as people with normal color vision. We hypothesize that a possible explanation for this discrepancy between color perception and behavioral consequences might be found in the gaze behavior of people with color vision deficiency. METHODS: A group of participants with color vision deficiencies and a control group performed several search tasks in a naturalistic setting on a lawn. All participants wore a mobile eye-tracking-driven camera with a high foveal image resolution (EyeSeeCam). Search performance as well as fixations of objects of different colors were examined. RESULTS: Search performance was similar in both groups in a color-unrelated search task as well as in a search for yellow targets. While searching for red targets, participants with color vision deficiencies exhibited a strongly degraded performance. This was closely matched by the number of fixations on red objects shown by the two groups. Importantly, once they fixated a target, participants with color vision deficiencies exhibited only few identification errors. CONCLUSIONS: In contrast to controls, participants with color vision deficiencies are not able to enhance their search for red targets on a (green) lawn by an efficient guiding mechanism. The data indicate that the impaired guiding is the main influence on search performance, while foveal identification (verification) is largely unaffected by the color vision deficiency.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".