Temporal dynamics of eye movements are related to differences in scene complexity and clutter
Bibliographic record
Abstract
Recent research has begun to explore not just the spatial distribution of eye fixations but also the temporal dynamics of how we look at the world. In this investigation, we assess how scene characteristics contribute to these fixation dynamics. In a free-viewing task, participants viewed three scene types: fractal, landscape, and social scenes. We used a relatively new method, recurrence quantification analysis (RQA), to quantify eye movement dynamics. RQA revealed that eye movement dynamics were dependent on the scene type viewed. To understand the underlying cause for these differences we applied a technique known as fractal analysis and discovered that complexity and clutter are two scene characteristics that affect fixation dynamics, but only in scenes with meaningful content. Critically, scene primitives-revealed by saliency analysis-had no impact on performance. In addition, we explored how RQA differs from the first half of the trial to the second half, as well as the potential to investigate the precision of fixation targeting by changing RQA radius values. Collectively, our results suggest that eye movement dynamics result from top-down viewing strategies that vary according to the meaning of a scene and its associated visual complexity and clutter.
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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 | 0.000 | 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 | 0.000 |
| 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 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".