Proactive suppression can be applied to multiple salient distractors in visual search.
Bibliographic record
Abstract
There is a growing body of research demonstrating that the capture of attention by a single salient distractor can be prevented via proactive suppression. In real-world contexts, there are often several distracting events that compete for attention, but it is entirely unknown whether multiple objects can be suppressed concurrently. We used behavioral and electrophysiological measures to investigate the existence and time course of multiple-item suppression. We employed search displays that contained either one or two uniquely colored distractors that differed in their salience (S+ and S-), or no such distractors. Search performance improved with the number of salient distractors, indicating that the suppression of multiple items reduced the effective display set size. This was also the case when the target color was no longer fully predictable, ruling out an alternative explanation in terms of attentional guidance by target templates. In an experiment where S+ and S- always appeared together in the same display, the PD component (a marker of proactive suppression) was triggered exclusively by the more salient distractor (S+), indicative of single-item suppression. However, when displays with one or both salient distractors were intermixed, a reliable PD component was also triggered by S-, even when it was accompanied by S+ in the same display. These results show that multiple concurrent salient signals can be proactively inhibited. They demonstrate that signal suppression processes can be adaptively employed to counteract visual distraction at different locations, in order to facilitate the attentional selection of relevant objects in crowded visual environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".