A new look at social attention: Orienting to the eyes is not (entirely) under volitional control.
Bibliographic record
Abstract
People tend to look at other people's eyes, but whether this bias is automatic or volitional is unclear. To discriminate between these two possibilities, we used a "don't look" (DL) paradigm. Participants looked at a series of upright or inverted faces, and were asked either to freely view the faces or to avoid looking at the eyes, or as a control, the mouth. As previously demonstrated, participants showed a bias to attend to both eyes and mouths during free viewing. In the DL condition, participants told to avoid the eyes of upright faces were unable to fully suppress the tendency to fixate on the faces' eyes, whereas participants told to avoid the mouth of upright faces successfully eliminated their bias to overtly attend to that feature. When faces were inverted, participants were equally able to suppress looks to the eyes and mouth. Together, these results suggest that the tendency to look at the eyes reflects orienting that is both volitional and automatic, and that the engagement of holistic or configural face processing mechanisms during upright face viewing has an influence in guiding gaze automatically to the 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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".