Bibliographic record
Abstract
We have previously shown that when observers are presented with complex natural scenes that contain a number of objects and people, observers look mostly at the eyes of the people. Why is this? It cannot be because eyes are merely the most salient area in a scene, as relative to other objects they are fairly inconspicuous. We hypothesized that people look at the eyes because they consider the eyes to be a rich source of information. To test this idea, we tested two groups of participants. One set of participants, called the Told Group, was informed that there would be a recognition test after they were shown the natural scenes. The second set, the Not Told Group, was not informed that there would be a subsequent recognition test. Our data showed that during the initial and test viewings, the Told Group fixated the eyes more frequently than the Not Told group, supporting the idea that the eyes are considered an informative region in social scenes. Converging evidence for this interpretation is that the Not Told Group fixated the eyes more frequently in the test session than in the study session.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".