Suppression of Salient Objects Prevents Distraction in Visual Search
Bibliographic record
Abstract
To find objects of interest in a cluttered and continually changing visual environment, humans must often ignore salient stimuli that are not currently relevant to the task at hand. Recent neuroimaging results indicate that the ability to prevent salience-driven distraction depends on the current level of attentional control activity in frontal cortex, but the specific mechanism by which this control activity prevents salience-driven distraction is still poorly understood. Here, we asked whether salience-driven distraction is prevented by suppressing salient distractors or by preferentially up-weighting the relevant visual dimension. We found that salient distractors were suppressed even when they resided in the same feature dimension as the target (that is, when dimensional weighting was not a viable selection strategy). Our neurophysiological measure of suppression--the PD component of the event-related potential--was associated with variations in the amount of time it took to perform the search task: distractors triggered the PD on fast-response trials, but on slow-response trials they triggered activity associated with working memory representation instead. These results demonstrate that during search salience-driven distraction is mitigated by a suppressive mechanism that reduces the salience of potentially distracting visual objects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".