The Active Suppression of a Distractor’s Location Can Be Elusive
Bibliographic record
Abstract
Our visual system is inundated with distracting objects that vie for our attention. While visual attention selects relevant information, inhibitory mechanisms might be useful to suppress the locations occupied by irrelevant distractors. Yet, there is a dearth of behavioral evidence for the active suppression of a distractor's location (ASDL) using central cues that provide preliminary information about a distractor's location. In the first two experiments, we attempt to conceptually replicate, using an online platform, experiments that provide evidence of the ASDL. We replicate the distractor cueing effect in a localization task (Experiment 1) wherein responses to targets were faster when a central arrow cued the location of an impending distractor than an empty location. This effect was larger in the first block of trials than it was in the second. In a discrimination task (Experiment 2), unlike previous studies, we found no evidence for an effect of distractor cueing. In Experiment 3, we replaced the central arrow cues with central number cues because arrow cues may elicit a symbolic shift of attention that might offset the ASDL. Once again, the best model was one in which the distractor cueing effect was absent. We replicate these failures to find evidence of the ASDL in two more experiments. The results suggest that the ASDL can be elusive and may be tied to the response system, not attention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".