Factors underlying inter-observer agreement in gaze patterns
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
In viewing an image or real-world scene, different observers may exhibit different viewing patterns. This is evidently due to a variety of different factors, involving both bottom-up and top-down processing. In the literature addressing prediction of visual saliency, agreement in gaze patterns across observers is often quantified according to a measure of inter-observer congruency (IOC). Intuitively, common viewership patterns may be expected to diagnose certain image qualities including the capacity for an image to draw attention, or perceptual qualities of an image relevant to applications in human computer interaction, visual design and other domains. Moreover, there is value in determining the extent to which different factors contribute to inter-observer variability, and corresponding dependence on the type of content being viewed. In this paper, we assess the extent to which different types of features contribute to variability in viewing patterns across observers. This is accomplished in considering correlation between image derived features and IOC values, and based on the capacity for more complex feature sets to predict IOC based on a regression model. Experimental results demonstrate the value of different feature types for predicting IOC. These results also establish the relative importance of top-down and bottom-up information in driving gaze and provide new insight into predictive analysis for gaze behavior associated with perceptual characteristics of images.
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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 | 0.000 |
| 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 it