Quantifying center bias of observers in free viewing of dynamic natural scenes
Bibliographic record
Abstract
Human eye-tracking studies have shown that gaze fixations are biased toward the center of natural scene stimuli ("center bias"). This bias contaminates the evaluation of computational models of attention and oculomotor behavior. Here we recorded eye movements from 17 participants watching 40 MTV-style video clips (with abrupt scene changes every 2-4 s), to quantify the relative contributions of five causes of center bias: photographer bias, motor bias, viewing strategy, orbital reserve, and screen center. Photographer bias was evaluated by five naive human raters and correlated with eye movements. The frequently changing scenes in MTV-style videos allowed us to assess how motor bias and viewing strategy affected center bias across time. In an additional experiment with 5 participants, videos were displayed at different locations within a large screen to investigate the influences of orbital reserve and screen center. Our results demonstrate quantitatively for the first time that center bias is correlated strongly with photographer bias and is influenced by viewing strategy at scene onset, while orbital reserve, screen center, and motor bias contribute minimally. We discuss methods to account for these influences to better assess computational models of visual attention and gaze using natural scene stimuli.
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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 | 0.005 |
| 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".