A dual mobile eye tracking study on natural eye contact during live interactions
Bibliographic record
Abstract
Human eyes convey a wealth of social information, with mutual looks representing one of the hallmark gaze communication behaviors. However, it remains relatively unknown if such reciprocal communication requires eye-to-eye contact or if general face-to-face looking is sufficient. To address this question, while recording looking behavior in live interacting dyads using dual mobile eye trackers, we analyzed how often participants engaged in mutual looks as a function of looking towards the top (i.e., the Eye region) and bottom half of the face (i.e., the Mouth region). We further examined how these different types of mutual looks during an interaction connected with later gaze-following behavior elicited in an individual experimental task. The results indicated that dyads engaged in mutual looks in various looking combinations (Eye-to-eye, Eye-to-mouth, and Mouth-to-Mouth) but proportionately spent little time in direct eye-to-eye gaze contact. However, the time spent in eye-to-eye contact significantly predicted the magnitude of later gaze following response elicited by the partner's gaze direction. Thus, humans engage in looking patterns toward different face parts during interactions, with direct eye-to-eye looks occurring relatively infrequently; however, social messages relayed during eye-to-eye contact appear to carry key information that propagates to affect subsequent individual social behavior.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".