The stare-in-the-crowd effect in the real world: is direct gaze really detected faster than averted gaze?
Bibliographic record
Abstract
Previous research has given support to the so-called “stare-in-the-crowd effect”, the notion that a direct gaze “pops out” in a crowd and can be more easily detected than averted gaze. This processing advantage is thought to be due to the importance of gaze contact for social interactions. However, these studies bore little ecological validity as they used search paradigms in which arrays of two-dimensional pairs of eyes were presented on a computer screen. The purpose of the present research was to investigate whether this processing advantage for direct gaze could be seen in more realistic settings such as in a virtual environment. Participants were required to locate the person with a direct (or averted) gaze presented amongst three other persons with averted (or direct) gaze. This was done either in 2D (on a flat computer screen), in 3D-no context (i.e. a blank virtual world) or in 3D with context (a virtual elevator). For the 3D conditions, participants wore a head-mounted display which immersed them in a virtual world. Results indicated slower reaction times when the task was done in three rather than two dimensions, and even slower RTs with the addition of meaningful context. No overall effect of target gaze was found but an interaction with target position was observed due to faster and more accurate detection of direct over averted gaze when targets were presented in the right visual field. When targets were in the far left visual field, however, the effect was reversed and averted gaze was more quickly detected than direct gaze. These findings suggest that detecting gaze direction in the real world mostly depends on spatial position. In other words, direct gaze does not always “pop-out”.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".