Dissecting Cognitive Operations in Difficult Visual Search Using Response-locked Posterior Contralateral Negativity Event-related Potentials
Bibliographic record
Abstract
We bisected the sequence of processing into operations taking place before or after the engagement of visual-spatial attention during a difficult search task using event-related potentials. We were able to assign variance in RTs associated with experimental factor effects to phases of processing by examining stimulus-locked (SLpcN) and response-locked (RLpcN) posterior contralateral negativity. Participants searched for a gray square with one gap among gray squares with two gaps. The number of displayed items (set size) and the number of response alternatives were varied. Both experimental manipulations affected the onset latency of the RLpcN, whereas the SLpcN showed small or no latency effects, suggesting they had effects after the initial deployment of attention. Moreover, amplitude effects in the RLpcN and SLpcN behaved similarly. Most importantly, different aspects of the RLpcN dissociated the experimental manipulations: Set size primarily affected processing between RLpcN onset and peak amplitude of the RLpcN, whereas the number of response alternatives affected the onset latency and the latency of peak amplitude of RLpcN. These results show how RLpcN activity can dissociate factor effects that are not separable with SLpcN activity during difficult search.
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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 | 0.004 |
| 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 | 0.000 |
| 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".