Effect of Sclera Size on Social Judgements: A Potential Support for the Cooperative Eye Hypothesis
Bibliographic record
Abstract
The cooperative eye hypothesis suggests that the morphological characteristics of the human eye have evolved to facilitate following other individuals’ gaze during social interactions. For example, both the white color and larger size of the sclera—the white part of the eye—facilitate gaze identification in humans. Furthermore, while sclera color has been shown to influence social perception such as attractiveness and health, there is much less data on the potential role of sclera size on social judgments. The objective of this study was to test the effect of sclera size on social judgments by comparing perceptions about other individuals with large vs. small sclera size. 108 men and 56 women were recruited in an online within-subject experimental paradigm. Participants had to judge using a 10-level scale the sociability, trustworthiness, social rank and physical attractiveness of pictures of faces taken from a validated database. The sclera size of 50 neutral human faces (25 female models, 25 male models) were digitally modified to have a version with a large sclera (44% of the total size of the eye) and a version with a small sclera (29% of the total size of the eye) resulting in 100 faces that were pseudo-randomly presented. Effect of sclera size, sex of the model, sex of the participant and their interactions on each social judgment was tested. Results show that social judgments about sociability, trustworthiness, and physical attractiveness were significantly more positive for faces with a large sclera than small sclera size. For social rank, we found a sclera size by sex of the participant interaction where only female participants perceived faces with larger sclera as having a higher social rank than faces with smaller sclera. These preliminary results, in line with the cooperative eye hypothesis, suggest a positive social bias towards faces with larger exposed sclera.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".