Divergent effects of positive and negative cueing on target enhancement and distractor suppression
Bibliographic record
Abstract
During visual search, one can use information about target features, such as color or shape, to guide attention (positive cueing). Attention can also be guided away from irrelevant items through active suppression of distractor features (negative cueing). Although previous studies have observed faster search times following a negative cue, when the task is relatively easy (i.e. smaller set size), negative cues tend to slow responses. It has been suggested that this is due to initial bottom-up attentional capture by the negatively cued feature, followed by its suppression (i.e. ‘search and destroy’ mechanism). Here, we aimed to better understand the time course of these cueing effects by examining event-related potentials related to target enhancement (N2pc) and distractor suppression (PD). Participants (N = 20) completed a lateralized visual search task wherein they had to find a target line within a colored circle. On each trial, participants were provided with a color cue indicating whether the target would be within the circle of that particular color, not within that color, or an uninformative cue. We found that participants could use positive cues to focus attention on the target item (as indicated by the N2pc) and suppress the distractor (as indicated by the PD). In contrast, when given a negative cue, participants inappropriately attended to the distractor color, followed by its active suppression. Ability to suppress the negatively cued distractor was related to individual differences in anxiety. These results provide electrophysiological evidence of the ‘search and destroy’ mechanism of negative search templates, and suggest that the ability to use negative cue information to benefit performance differs across individuals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".