Bibliographic record
Abstract
The present study examined the effect of scene context on guidance of attention during visual search in real‐world scenes. Prior research has demonstrated that when searching for an object, attention is usually guided to the region of a scene that most likely contains that target object. This study examined two possible mechanisms of attention that underlie efficient search: enhancement of attention (facilitation) and a deficiency of attention (inhibition). In this study, participants (N=20) were shown an object name and then required to search through scenes for the target while their eye movements were tracked. Scenes were divided into target‐relevant contextual regions (upper, middle, lower) and participants searched repeatedly in the same scene for different targets either in the same region or in different regions. Comparing repeated searches within the same scene across different regions, we expect to find that visual search is faster and more efficient (facilitation of attention) in regions of a scene where attention was previously deployed. At the same time, when searching across different regions, we expect searches to be slower and less efficient (inhibition of attention) because those regions were previously ignored. Results from this study help to better understand how mechanisms of visual attention operate within scene contexts during visual search.
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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 | 0.007 |
| 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 | 0.001 | 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".