The deployment of visual spatial attention during visual search predicts response time
Bibliographic record
Abstract
We tracked the deployment of visual spatial attention, as indexed by an electrophysiological event-related potential named the N-2-posterior-contralateral (N2pc). We expected that a stronger and/or earlier deployment of attention would predict faster responses in a visual search task. We tested this hypothesis by sorting the electrophysiological segments into two categories (slow vs. fast) by trial-by-trial response times (RTs), for each participant, on the basis of the median RT within each condition of the experiment. We also classified participants on the basis of overall mean RTs into those faster than the group median and those slower than the group median. The N2pc was larger and earlier for fast responders compared with slow responders. Furthermore, within each of these groups, faster responses were associated with a larger and earlier N2pc. These results provide further evidence that the N2pc is a valid index of the deployment of visual attention, and suggest that a more effective deployment of visual spatial attention (larger and/or earlier N2pc) predicts a faster response, both within and between subjects.
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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 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".