The role of clarity and blur in guiding visual attention in photographs.
Bibliographic record
Abstract
Visual artists and photographers believe that a viewer's gaze can be guided by selective use of image clarity and blur, but there is little systematic research. In this study, participants performed several eye-tracking tasks with the same naturalistic photographs, including recognition memory for the entire photo, as well as recognition memory and personality ratings for individual people in the photos (Experiments 1-3). The results showed that fixations occurred more rapidly and frequently to a local region of clarity than to a comparable blurred region in all tasks, independent of the content of the photo in the local region, and even under instructions to look equally at both regions. However, this bias was reversed when the content of the photos was no longer task-relevant. In Experiment 4, participants located target regions defined by either clarity or blur. Fixations and manual responses were faster for blurred than for sharp targets. These findings imply that the saliency of both image clarity and image blur depends on viewers' goals. Focusing on photo content prioritizes regions of clarity whereas focusing on photo quality prioritizes attention to regions of blur.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".