Enhance or inhibit? Behavioral and ERP effects of distractor memory on attentional competition
Bibliographic record
Abstract
According to competition models of attention, two items compete for a cell's response when they both fall within the receptive field of that cell. This competition can be measured behaviorally as an increase in the response time to one target as its proximity to a distractor increases. This competition can also be observed in two event-related potentials (ERPs): the canonical parietal N2pc increases as a target's distance increases from a distractor, whereas the more temporal Ptc is largest when targets and distractors are very close. Thus, these components are thought to reflect different processes of resolving competition, with the N2pc involved in target selection, and the Ptc thought to reflect processes of target enhancement. Here, we examine the effect of having prior exposure to distractors on behavioral and ERP measures of attentional competition. Participants had to report the orientation of a target, and the distance between the target and a distractor was manipulated. In addition, on half the trials, participants were first given a 200 ms “preview” of the distractor. Thus, participants could use memory to remember and inhibit the distractor on the subsequent search display. The results indicate that although RTs were faster in the preview condition, they were still modulated by the spatial separation between the target and distractor, such that RTs were slowest when the distractor was next to the target. We also observed a reduction in the amplitude of the Ptc in the preview condition, relative to the “no preview” condition. This suggests that prior information about the distractor makes target processing easier. These findings indicate that the inhibition of the distractor may aid in subsequent target enhancement, although competition between targets and distractors is not entirely resolved.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".