Bibliographic record
Abstract
Eye contact requires attention both when we send and receive gaze signals. Previous research suggests that when one is attending to something their perception of time is altered, such that time passes by more slowly while watching a pot boil. The disruption of time perception has been shown to happen during face-to-face eye contact but has also been observed (albeit to a lesser extent) if one person is looking at another or being looked at by another [Jarick et al., 2016]. Here, we aimed to tease apart whether eye contact is more attention-capturing when we are sending signals during mutual gaze or receiving the gaze signal, or both. This will be investigated by having pairs of participants (sitting side-by-side) make subjective time estimates of 40, 60, and 80 seconds during the participation of four gaze trials: looking away from one another (baseline), looking at the profile of their partner, being looked at by their partner and making eye contact. If attention is equally attributed to sending and receiving signals, we predict that the degree to which time estimation is disrupted during the profile and looked at trials will sum to the disruption found during eye contact trials. Alternatively, if attention is captured more by sending or receiving gaze signals, then we should see time estimation more disrupted in either the profile or looked at trials. This research will allow us to further understand how attention is allocated during face-to-face eye contact in the wild. Discipline: Psychology Faculty Mentor: Dr. Michelle Jarick
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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 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".