The brightness of a looker's iris is not important in determining direction of gaze
Bibliographic record
Abstract
A number of recent reports suggest that reversing the polarity of the iris and sclera affects the ability to determine the direction of gaze in a digitized photo (e.g. Ricciardelli et al., 2000). Ando (2002) has suggested that direction of gaze might be determined by assessing the relative brightness of the sclera on each side of the iris. These findings suggest that the absolute brightness of the iris is less crucial in determining direction of gaze, as long as it is darker than the sclera. The present study assessed the impact of iris brightness on the ability to determine direction of gaze in a task similar to that of Ricciardelli et al. Observers were required to indicate whether a digitized “looker” was looking to the left, right or directly at them. For any given trial, the iris of the looker's eyes ranged in 10 steps from very dark (0.98 cd/m2) to very bright (58.5 cd/m2). The brightness of the sclera remained constant at 19 cd/m2. The observers' accuracy was consistently high for a wide range of iris brightnesses, as long as the iris was darker than the sclera. Importantly, when the iris was brighter than the sclera accuracy was consistently disrupted, regardless of the absolute brightness of the iris. The results suggest that contrast polarity between iris and sclera is important in determining direction of gaze, while absolute brightness of the iris is ignored. Presumably, such an all-or-none process carries several evolutionary advantages with it, such as facilitating efficient communication in the real world where different individuals have different colored eyes.
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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 | 0.001 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".