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 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 | 0.000 |
| 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".