The Role of Photographic Clarity and Blur in Guiding Visual Attention
Bibliographic record
Abstract
Although visual artists and photographers know that a viewer’s gaze can be guided by selective use of image clarity and blur, there has been little systematic research. For example, there are hints that clarity and blur may play different roles in guiding attention when they are implicit cues to attention (Veas et al., CHI 2010) versus when they are the explicit targets of a task (Kosara et al., VRVis 2001) but systematic comparisons have not been done. In this study, 24 participants performed two eye-tracking tasks with the same naturalistic photographs, a recognition memory task in which manipulated image regions were not mentioned to participants (Experiment 1), followed by a visual search task in which these regions were explicit targets (Experiment 2). The results from the implicit task in Experiment 1 showed that fixations occurred more rapidly and frequently to a local region of clarity in a photo than to a comparable blurred region. However, this bias was completely reversed in the visual search task of Experiment 2, where fixations and manual responses were faster to targets that were blurred than to those that were sharp. These findings emphasize that the spontaneous tendency of viewers’ eyes to seek out regions of clarity in a photo does not mean image blur is ignored. Rather, the exquisite sensitivity to blur is evidence that these signals are integral to the strong association in everyday vision between image clarity (achieved through accommodation, vergence, and foveation) and focused attention (achieved through directed gaze). Meeting abstract presented at VSS 2012
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".