Gaze behaviour in the natural environment: Eye movements in video versus the real world
Bibliographic record
Abstract
How do people distribute their visual attention in the natural environment? This question is often addressed by showing pictures, photographs or videos of natural scenes under controlled conditions and recording participants' eye movements as they view them. In this experiment, we investigated whether people distribute their gaze in the same way when they are walking around the real world as when they view video clips taken from the perspective of a walker. We hypothesized that, due to being immersed in the real environment, people would look at different items and change their scanning strategy when actually walking around the scene as opposed to passively watching a video of it. In the first session, participants walked at their own pace between two points on a university campus, and their point of gaze was recorded using a discrete portable eye-tracker. In a subsequent session, both these and a new set of participants viewed video clips of the walk that were captured by the eye-tracker. These clips showed the first-person perspective of someone walking around campus, and participants were asked to watch the videos as if they were walking the route themselves, while sitting at a computer monitor. The most inspected items were other people, obstacles and the path ahead. This was particularly the case when people were actually walking, although the tendency to look at people was modified by the social situation. These results provide important evidence that gaze behaviour is determined by an interaction between individuals and their environment, and our findings help to bridge the gap between attention in the laboratory and in the real world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".