Modelling the influence of central and peripheral information on saccade biases in gaze-contingent scene viewing
Why this work is in the frame
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Bibliographic record
Abstract
Human saccades during scene viewing show systematic patterns in amplitude and direction. By using a gaze-contingent display, it is possible to manipulate the features available for planning these saccades by masking the peripheral visual field. Here, we propose several variations of a computational model for predicting the saccade statistics observed empirically during viewing with different gaze-contingent displays. In each case, saccade targets are generated by randomly sampling from a distribution computed according to either the features available at fixation, the intact information in the periphery, or combinations of the two. The results suggest that saccade generation in complex images results from a balance of these computations, and the model provides a simple but rigorous framework for testing hypotheses and making novel predictions about the spatial characteristics of eye movements.
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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 | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 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