The Moving Eye is Easy to Spy: How Motion Improves Gaze Discrimination
Bibliographic record
Abstract
Research indicates that people are remarkably good at discriminating where another person is looking (Gibson & Pick, 1963; Bock, Dicke, & Thier, 2008). Indeed, theories of human social attention are predicated on the idea that humans have developed an especially fine ability to use the eyes of others to make inferences about their attentional state (Kobayashi & Kohshima, 1997). Although this research demonstrates the importance of eyes to attention, little work has examined the specific mechanisms that underlie gaze discrimination itself. The prevailing view is that the discrimination of gaze direction relies on the use of the ratio of iris to sclera in the visible part of the eye (Gibson & Pick, 1963; Olk, Symons, & Kingstone, 2008). This theory is based on research that has used static images of eye direction. In real life, however, a change in eye position involves the motion of the eyes themselves as well as a change in the iris:sclera ratio. We examined the role of eye motion in the discrimination of eye movements. Participants were shown two eye images: eyes looking straight ahead and then eyes looking left or right at 1, 2 or 3 degrees visual angle from fixation. This resulted in the apparent motion of the eyes to the left or right. A 200 ms ‘blank screen’ preceded or followed these eye motion conditions. In the no-motion condition, the 200ms ‘blank’ display was inserted between the first and second eye image, eliminating the perception of motion. In all cases participants were required to judge eye direction and to rate how confident they were of their decision. Participants were more accurate and more confident of their judgements in the eye motion condition. These data suggest that motion information is used by the perceptual system to determine the direction of another individual's gaze.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".