Electrophysiological Indices of Target and Distractor Processing in Visual Search
Bibliographic record
Abstract
Attentional selection of a target presented among distractors can be indexed with an event-related potential (ERP) component known as the N2pc. Theoretical interpretation of the N2pc has suggested that it reflects a fundamental mechanism of attention that shelters the cortical representation of targets by suppressing neural activity stemming from distractors. Results from fields other than human electrophysiology, however, suggest that attention does not act solely through distractor suppression; rather, it modulates the processing of both target and distractors. We conducted four ERP experiments designed to investigate whether the N2pc reflects multiple attentional mechanisms. Our goal was to reconcile ostensibly conflicting outcomes obtained in electrophysiological studies of attention with those obtained using other methodologies. Participants viewed visual search arrays containing one target and one distractor. In Experiments 1 through 3, the distractor was isoluminant with the background, and therefore, did not elicit early lateralized ERP activity. This work revealed a novel contralateral ERP component that appears to reflect direct suppression of the cortical representation of the distractor. We accordingly name this component the distractor positivity (P(D)). In Experiment 4, an ERP component associated with target processing was additionally isolated. We refer to this component as the target negativity (N(T)). We believe that the N2pc reflects the summation of the P(D) and N(T), and that these discrete components may have been confounded in earlier electrophysiological studies. Overall, this study demonstrates that attention acts on both target and distractor representations, and that this can be indexed in the visual ERP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".