Bibliographic record
Abstract
It has been well established that attentional control sets occur when observers search the visual field for targets defined by a specific feature. When uninformative peripheral cues precede the target, cues that contain the specific target feature will capture attention while cues that do not will be effectively ignored. We tested whether different attentional control sets can be simultaneously maintained over distinct regions of space. On each trial, either a blue or a green target was presented within one of two placeholders located to the left and to the right of a central fixation cross. Observers were instructed to respond to only specific colored targets at specific locations (i.e., blue target at left placeholder, green target at right placeholder, or vice versa). On most trials, 150 ms before the target was presented, either a blue or a green cue appeared around the left or the right placeholder. These cues varied on two dimensions in relation to the impending targets: they were presented either in the same location (valid) or the opposite location (invalid), and they were either the same color (matching) or the other color (nonmatching). On other trials, no cue was presented. All trial types occurred with equal probability, and response time (RT) to the target was recorded. As expected, trials in which the cue was congruent with the target (valid-matching) elicited the fastest RTs relative to no-cue trials, while fully incongruent trails (invalid-nonmatching) elicited the longest RTs. Importantly, the partially incongruent trials (valid-nonmatching and invalid-matching) elicited RTs that did not differ from no-cue trials. This is the first demonstration that two separate attentional control sets can be simultaneously maintained at distinct spatial locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.031 |
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".