Neural Mechanisms of Surround Attenuation and Distractor Competition in Visual Search
Bibliographic record
Abstract
Visual attention biases relevant processing in the visual system by amplifying relevant or attenuating irrelevant sensory input. A potential signature of the latter operation, referred to as surround attenuation, has recently been identified in the electromagnetic brain response of human observers performing visual search. It was found that a zone of attenuated cortical excitability surrounds the target when the search required increased spatial resolution for item discrimination. Here we address the obvious hypothesis that surround attenuation serves distractor suppression in the vicinity of the target where interference from irrelevant search items is maximal. To test this hypothesis, surround attenuation was assessed under conditions when the target was presented in isolation versus when it was surrounded by distractors. Surprisingly, substantial and indistinguishable surround attenuation was seen under both conditions, indicating that it reflects an attentional operation independent of the presence of distractors. Adding distractors in the target's surround, however, increased the amplitude of the N2pc--an evoked response known to index distractor competition in visual search. Moreover, adding distractors led to a topographical change of source activity underlying the N2pc toward earlier extrastriate areas. In contrast, the topography of reduced source activity due to surround attenuation remained unaltered with and without distractors in the target's surround. We conclude that surround attenuation is not a direct consequence of the attenuation of distractors in visual search and that it dissociates from attentional operations reflected by the N2pc. A theoretical framework is proposed that links both operations in a common model of top-down attentional selection in visual cortex.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".