Are fixations in static natural scenes a useful predictor of attention in the real world?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research investigating scene perception normally involves laboratory experiments using static images. Much has been learned about how observers look at pictures of the real world and the attentional mechanisms underlying this behaviour. However, the use of static, isolated pictures as a proxy for studying everyday attention in real environments has led to the criticism that such experiments are artificial. We report a new study that tests the extent to which the real world can be reduced to simpler laboratory stimuli. We recorded the gaze of participants walking on a university campus with a mobile eye tracker, and then showed static frames from this walk to new participants, in either a random or sequential order. The aim was to compare the gaze of participants walking in the real environment with fixations on pictures of the same scene. The data show that picture order affects interobserver fixation consistency and changes looking patterns. Critically, while fixations on the static images overlapped significantly with the actual real-world eye movements, they did so no more than a model that assumed a general bias to the centre. Remarkably, a model that simply takes into account where the eyes are normally positioned in the head-independent of what is actually in the scene-does far better than any other model. These data reveal that viewing patterns to static scenes are a relatively poor proxy for predicting real world eye movement behaviour, while raising intriguing possibilities for how to best measure attention in everyday life. (PsycINFO Database Record
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it