Don't Look Now: The influence of distractor features vs. spatial relevance on attentional deployment
Bibliographic record
Abstract
Visual search performance is aided by knowledge of scene context and likely target positioning (Castelhano & Henderson, 2007; Neider & Zelinsky, 2006). In a recent study, Pereira and Castelhano (2017) demonstrated that attention is differentially deployed depending on the target-relevance of each scene region: in an abrupt-onset paradigm, target-relevant distractors were fixated upon and saccaded towards significantly more often than target-irrelevant distractors. In the present study, we examined whether the visual features of distractors influence attentional deployment over and above the spatial relevance of their positions. Distractors were placed in scene regions that were operationalized as either target-relevant or target-irrelevant, and were either visually similar or dissimilar to the target object. Participants saccaded towards and fixated upon target-relevant distractors significantly more often than target-irrelevant distractors. Interestingly, visual target-distractor similarity did not have an effect: only distractors appearing within target-relevant regions reliably attracted attention, regardless of their visual similarity to the target. These findings suggest that attention during search is distributed based on likely target positioning and, surprisingly, that attentional capture within scenes may be better predicted by spatial relevance than by visual feature similarity. However, previous research has also demonstrated that distractors are more likely to capture attention when they share categorical features with the target (Wyble, Folk, & Potter, 2013). We will further examine the potential interaction between the spatial relevance of distractor positioning and categorical target-distractor similarity, in order to assess the extent to which spatial relevance and distractor features differentially predict attentional deployment in search through real-world scenes. Meeting abstract presented at VSS 2018
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".