Does Context act like a Spatial Attentional Set?: Exploring attentional control during visual search in scenes.
Bibliographic record
Abstract
Attentional control and attentional sets (Folk & Remington, 1994) have been extensively studied in visual search arrays as a selectivity mechanism for filtering out irrelevant information. In the current study, we explore whether such a mechanism could also exist during visual search in scenes. Research has shown that when people search for an object in a scene, they use context to narrow their search to specific regions where the target is likely to be found. The current study investigates whether context can act as a spatial attentional set, where different areas of the scene become more or less relevant depending on the search target. Participants searched for a target object in 24 real world scenes while their eye movements were tracked. On 50% of the trials, an irrelevant distractor object would unexpectedly appear. The distractor would onset 50ms after the first fixation began and would appear either in the target’s context region (Within condition) half the time or in a different context region (Outside condition). The relevant scene context was based on the general placement of target object (lower, middle and upper regions). Fixation data was used to determine the proportion of trials in which a participant immediately fixated on the distractor (within 2 fixations). We compared the proportion of fixations in the Within and Outside conditions and found they were significantly more likely to immediately fixate on the distractor when it was presented Within context (57%) than Outside (33%, p<.001). These results are in line with previous work showing that sudden onsetting objects do capture attention in scenes (Brockmole & Henderson, JEP:HPP, 2005). However, the current study shows that this ability to capture attention is dependent on attentional settings and suggests scene context is used to focus attention on target object regions and suppress information from other regions. Meeting abstract presented at VSS 2012
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".