Unpredictable singleton distractors in visual search can be subject to second-order suppression
Bibliographic record
Abstract
Recent evidence suggests that attentional capture by salient-but-irrelevant distractions can be avoided via suppression, thereby improving performance in visual search. Initial evidence suggested it is only possible to suppress salient distractors with constant and predictable features (first-order suppression). We show that previous failures to find evidence for second-order suppression of unpredictable feature singletons may have been due to low feature variability: If it is probable that the salient distractor colour is the target colour on another trial, suppressing this item might hinder performance. We first validated a new multiframe letter-probe paradigm, where observers counted the search displays with a target shape and always reported as many letter probes as possible from the final display. When target and singleton colours were constant (Experiment 1), a singleton suppression effect was observed, with probe letters at the singleton distractor location reported less frequently than those at non-singleton distractor locations. When two randomly swapped target/singleton colours were employed (Experiment 2), no suppression effect was observed, replicating previous findings. Critically, when target-colour items and the singleton could have one of eight different random colours (Experiment 3), a robust suppression effect reappeared. These observations demonstrate that first-order suppression is not universal, and that second-order suppression can be applied to singleton distractors under some circumstances. Suppression effects were observed for displays with and without targets, suggesting that they are not a product of direct target-singleton competition.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".