Changing the speed and order of attentional selection in visual search
Bibliographic record
Abstract
Seminal event-related potential (ERP) studies of visual search reported that young adults serially inspect two singletons when searching for a target (serial search), but later results showed that the second singleton can be selected while the first singleton is still attended (partially parallel search). These contrasting results indicate that some yet-to-be identified factor can affect the speed of search. We hypothesized that single-target detection tasks promote serial inspection while dual-target comparison tasks promote parallel inspection. We recorded ERP activities associated with attentional selection (N2pc) and subsequent identification (SPCN) to track attentional processing of two singletons while healthy young adults participated in one of two detection tasks or a comparison task. One singleton was made to be more salient than the other to give it a "natural" selection advantage and thus promote some serial processing even in the comparison task. The timing of N2pc activities indicated that attention was deployed to the second singleton more quickly when participants compared the orientations of lines inside the two singletons than when they searched for one specific line that was more likely to be positioned inside one singleton or the other. Surprisingly, however, search was never fully serial, even in detection tasks that encouraged close inspection of individual items. Rather, in such detection tasks, items were selected serially but were processed for identification concurrently (as indexed by the SPCN). These findings are consistent with serial-parallel hybrid models of visual 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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".