Emphasizing responder speed or accuracy modulates but does not abolish the distractor-induced quitting effect in visual search
Bibliographic record
Abstract
When a highly salient distractor is present in a search array, it speeds target absent visual search and increases errors during target present visual search, suggesting lowered quitting thresholds (Moher in Psychol Sci 31(1):31-42, 2020). Missing a critical target in the presence of a highly salient distractor can have dire consequences in real-world search tasks where accurate target detection is crucial, such as baggage screening. As such, the current study examined whether emphasizing either accuracy or speed would eliminate the distractor-generated quitting threshold effect (QTE). Three blocks of a target detection search task which included a highly salient distractor on half of all trials were used. In one block, participants received no instructions or feedback regarding performance. In the remaining two blocks, they received instructions and trial-by-trial feedback that either emphasized response speed or response accuracy. Overall, the distractor lowered quitting thresholds, regardless of whether response speed or response accuracy was emphasized in a block of trials. However, the effect of the distractor on target misses was smaller when accuracy was emphasized. It, therefore, appears that while the distractor QTE is not easily eradicated by explicit instructions and feedback, it can be shifted. As such, future research should examine the applicability of these and similar strategies in real-world search scenarios.
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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 | 0.003 |
| 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".