Distractor size matters: Distractors may, or may not, speed target-absent searches
Bibliographic record
Abstract
Years of research have dissected the effects of salient distractors on visual search efficiency, typically finding that distractors slow visual search and increase error rates when detecting targets. Nonetheless, Moher (2020) recently demonstrated that the effects of salient distractors on visual search depend on target presence/absence. Specifically, when targets were present, Moher found that distractors slowed search speeds and increased error rates. However, when targets were absent, search speeds decreased, suggesting a strategy change in observers that lowered the quitting thresholds for target-absent visual search. This counterintuitive finding appears to depend on the salience of the distractor. Replicating Moher (2020), we found that when the distractor was much larger than the other items, search speeds for target-absent trials were faster compared to when there was no distractor. In contrast, when a smaller but still salient distractor was used, search speeds for target-absent trials were slower compared to when the distractor was absent. Note that in both experiments the distractor was a different color than the other items. Potential reasons for these qualitatively divergent findings include the distance between the salient distractor and other search items or a shift in search strategy, which only emerges with very high salience distractors.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".