Tracking target and distractor processing in visual search: Evidence from human electrophysiology
Bibliographic record
Abstract
The issue of whether salient distractors capture attention has been contentious for over 20 years. According to the salience-driven selection theory, the most salient location in the display is detected preattentively, after which attention is deployed automatically to that location. By other accounts, attentional deployment to the location of an item is contingent upon the task-relevance of that item. In the present work, six experiments employed the event-related potential (ERP) technique to examine the salience-driven selection and other theories of visual search. The experiments adopted additional singleton search, pop-out detection, and attentional-window paradigms. The ERP evidence obtained from the additional-singleton paradigm indicated that although the location of a salient item – whether a target or a distractor – was registered relatively early, the salient distractor did not capture attention consistently. Moreover, when the features of the salient distractor were held constant, observers were occasionally able to suppress the location of the distractor, thereby improving the efficiency of the search. The ERP evidence obtained from a Go/No-Go pop-out detection task indicated that attention was deployed to the location of a pop-out item only when a decision to search was made and, thus, that item was relevant to the observer’s goals. The ERP evidence obtained from the attentional-window paradigm indicated that goal-driven control over stimulus salience could extend to the items located within the observer’s attentional window. The present results suggest that while the locations of a limited number of salient items in the display can be registered on an early salience map, there is some goal-driven control over attentional deployment to the location of salient items or suppression of such locations. Factors that are potentially important in this dynamic control include the task-relevance of the search display, the predictability of distractor features, and inter-trial changes in target and distractor features and their task-relevance.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".