Gaze in Visual Search Is Guided More Efficiently by Positive Cues than by Negative Cues
Bibliographic record
Abstract
Visual search can be accelerated when properties of the target are known. Such knowledge allows the searcher to direct attention to items sharing these properties. Recent work indicates that information about properties of non-targets (i.e., negative cues) can also guide search. In the present study, we examine whether negative cues lead to different search behavior compared to positive cues. We asked observers to search for a target defined by a certain shape singleton (broken line among solid lines). Each line was embedded in a colored disk. In "positive cue" blocks, participants were informed about possible colors of the target item. In "negative cue" blocks, the participants were informed about colors that could not contain the target. Search displays were designed such that with both the positive and negative cues, the same number of items could potentially contain the broken line ("relevant items"). Thus, both cues were equally informative. We measured response times and eye movements. Participants exhibited longer response times when provided with negative cues compared to positive cues. Although negative cues did guide the eyes to relevant items, there were marked differences in eye movements. Negative cues resulted in smaller proportions of fixations on relevant items, longer duration of fixations and in higher rates of fixations per item as compared to positive cues. The effectiveness of both cue types, as measured by fixations on relevant items, increased over the course of each search. In sum, a negative color cue can guide attention to relevant items, but it is less efficient than a positive cue of the same informational value.
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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 | 0.004 |
| 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) | 0.002 | 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".