What is the shape of the visual information that drives saccades in natural images? Evidence from a gaze-contingent display
Bibliographic record
Abstract
The decision of where to move the eyes in natural scenes is influenced by both image features and the task at hand. Here, we consider how the information at fixation affects some of the biases typically found in human saccades. In an encoding task, people tend to show a predominance of horizontal saccades. Fixations are often biased towards the centre of the image, and saccade amplitudes show a characteristic distribution. How do these patterns change when peripheral regions are masked or blurred in a gaze-contingent moving window paradigm? In two experiments we recorded eye movements while observers inspected natural scenes in preparation for a recognition test. We manipulated the shape of a window of preserved vision at fixation: features inside the window were intact; peripheral background was either completely masked (Experiment 1) or blurred (Experiment 2). The foveal window was square, or rectangular or elliptical, with more preserved information either horizontally or vertically. If saccades function to increase the new information gained on each fixation, a horizontal window should lead to more vertical saccades and vice versa. In fact, we found the opposite pattern: vertical windows led to more vertical saccades, and horizontal windows were more similar to normal, unconstrained viewing. The shape of the window also affected fixation and amplitude distributions. These results suggest that saccades are influenced by the features currently being processed, rather than by a desire to reveal new information, and that in normal vision these features are sampled from a horizontally elongated region. The eyes would rather continue to explore a partially seen region than launch into the unknown.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".