Suppression of a salient distractor protects the processing of target features
Bibliographic record
Abstract
We are often bombarded with salient stimuli that capture our attention and distract us from our current goals. Decades of research has shown the robust detrimental impacts of salient distractors on search performance and, of late, in leading to altered feature perception. These feature errors can be quite extreme, and thus, undesirable. In search tasks, salient distractors can be suppressed if they appear more frequently in one location, and this learned spatial suppression can lead to reductions in the cost of distraction as measured by reaction time slowing. Can learned spatial suppression also protect against visual feature errors? To investigate this question, participants were cued to report one of four briefly presented colored squares on a color wheel. On two-thirds of trials, a salient distractor appeared around one of the nontarget squares, appearing more frequently in one location over the course of the experiment. Participants' responses were fit to a model estimating performance parameters and compared across conditions. Our results showed that general performance (guessing and precision) improved when the salient distractor appeared in a likely location relative to elsewhere. Critically, feature swap errors (probability of misreporting the color at the salient distractor’s location) were also significantly reduced when the distractor appeared in a likely location, suggesting that learned spatial suppression of a salient distractor helps protect the processing of target features. This study provides evidence that, in addition to helping us avoid salient distractors, suppression likely plays a larger role in helping to prevent distracting information from being encoded.
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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 | 0.002 |
| 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".