The duality of gaze: eyes extract and signal social information during sustained cooperative and competitive dyadic gaze
Bibliographic record
Abstract
In contrast to non-human primate eyes, which have a dark sclera surrounding a dark iris, human eyes have a white sclera that surrounds a dark iris. This high contrast morphology allows humans to determine quickly and easily where others are looking and infer what they are attending to. In recent years an enormous body of work has used photos and schematic images of faces to study these aspects of social attention, e.g., the selection of the eyes of others and the shift of attention to where those eyes are directed. However, evolutionary theory holds that humans did not develop a high contrast morphology simply to use the eyes of others as attentional cues; rather they sacrificed camouflage for communication, that is, to signal their thoughts and intentions to others. In the present study we demonstrate the importance of this by taking as our starting point the hypothesis that a cornerstone of non-verbal communication is the eye contact between individuals and the time that it is held. In a single simple study we show experimentally that the effect of eye contact can be quickly and profoundly altered merely by having participants, who had never met before, play a game in a cooperative or competitive manner. After the game participants were asked to make eye contact for a prolonged period of time (10 min). Those who had played the game cooperatively found this terribly difficult to do, repeatedly talking and breaking gaze. In contrast, those who had played the game competitively were able to stare quietly at each other for a sustained period. Collectively these data demonstrate that when looking at the eyes of a real person one both acquires and signals information to the other person. This duality of gaze is critical to non-verbal communication, with the nature of that communication shaped by the relationship between individuals, e.g., cooperative or competitive.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".