A Longer Look at Time: Time Slows Down During Prolonged Eye Contact
Bibliographic record
Abstract
Eye contact plays a crucial role during social interactions. It demonstrates that we are engaged, interested, and attending to the people involved. Why then, do we only make eye contact for brief periods of time? One hypothesis is that prolonged eye contact (> 5 seconds) elicits an elevated degree of arousal, which then could be interpreted as intimacy (or intimidation) leading to anxiety in both individuals. Here we investigated whether prolonged eye contact (live or through the computer) could distort our perception of time. In the live condition, two naïve participants made a 1-minute time estimate while sitting next to one another and maintaining three different poses: looked at the wall (baseline), looked at their partner’s profile (face averted) or made eye contact with their partner. Over the computer, participants made the 1-minute estimate while watching videos equated to the live poses (i.e., empty room, person’s profile, and eye contact). Recent research has shown that subjective time estimates increase (i.e., time slows down) during arousing events. Thus, if eye contact induces a high degree of arousal, then we predicted time would seem to slow down during conditions in which participants made eye contact. Indeed this was the case. We found that participants made significantly longer time estimates when they made eye contact, as opposed to when they looked at another person or just sat next to another person. Importantly, this duration expansion was only observed when participants made eye contact with their partner face-to-face and not with a person in a video. We attribute this difference to an increase in anxiety when looking into the eyes of a person next to you that does not exist over the computer. Thus, we showed that when studied in a natural environment, prolonged eye contact causes time to slow down. Meeting abstract presented at VSS 2012
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".