Attentional control settings determine not only what captures attention, but where attention goes once captured
Bibliographic record
Abstract
Is attention automatically captured to the location of salient stimuli, or is capture under our control? The best evidence that capture can be controlled comes from contingent capture in attention cueing tasks: When looking for a visual target (e.g., a red target), distracting stimuli only capture attention if they resemble the target (e.g., a task-irrelevant red pre-cue). Put differently, what observers are doing in the target display determines which types of features will capture attention in the cue display. Here we assessed another level of control. What participants are doing in the target display may also determine where attention goes in response to the cue in the cue display. This prediction draws from a recent demonstration that spatial attention can be cued to arbitrary locations based on implicitly learned associations between features and locations (Girardi & Nico, 2017). In the present experiments, participants completed a cueing task where on every trial a target was presented to the left and right of fixation, and a separate, coloured stimulus indicated which target the participant should report (e.g., red meant report left target; green report right). Thus, the target display created an association between colours and shifting attention to the left or right. Across three experiments, task-irrelevant, non-predictive pre-cues captured attention to the location associated with their colour (e.g., red cues captured attention to the left location) regardless of where the cue physically appeared in space. These experiments also investigated and ruled out two alternative explanations based on spatially specific attentional control settings and colour priming. Instead, the present findings support the conclusion that attentional control settings do not only determine what types of stimuli should capture visual spatial attention, but also define where attention should go when captured.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".