Tracking target and distractor processing in fixed-feature visual search: Evidence from human electrophysiology.
Bibliographic record
Abstract
Salient distractors delay visual search for less salient targets in additional-singleton tasks, even when the features of the stimuli are fixed across trials. According to the salience-driven selection hypothesis, this delay is due to an initial attentional deployment to the distractor. Recent event-related potential (ERP) studies found no evidence for salience-driven selection in fixed-feature search, but the methods employed were not optimized to isolate distractor ERP components such as the N2pc and distractor positivity (PD; indices of selection and suppression, respectively). Here, we isolated target and distractor ERPs in two fixed-feature search experiments. Participants searched for a shape singleton in the presence of a more-salient color singleton (Experiment 1) or for a color singleton in the presence of a less-salient shape singleton (Experiment 2). The salient distractor did not elicit an N2pc, but it did elicit a PD on fast-response trials. Furthermore, distractors had no effect on the timing of the target N2pc. These results indicate that (a) the distractor was prevented from engaging the attentional mechanism associated with N2pc, (b) the distractor did not interrupt the deployment of attention to the target, and (c) competition for attention can be resolved by suppressing locations of irrelevant items on a salience-based priority map.
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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 | 0.000 | 0.005 |
| 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 | 0.001 |
| 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".