Attentional control through colour-location associations induces contingent capture
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., an orange target), distracting stimuli only capture attention if they resemble the target (e.g., a non-predictive orange onset pre-cue). Put differently, an observer’s attentional goals during the target display determine which types of features capture attention in the cue display. Our recent work assessed another level of control and found that target-related goals also determine where attention goes in response to the cue. In this work, participants completed a spatial-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., orange meant report left target; green report right). Thus, the target display created an association between colours and shifting attention to the left or right. The non-predictive pre-cues captured attention to the location associated with their colour (e.g., orange cues captured attention to the left location) regardless of where the cues physically appeared in space. Does this type of attentional control through colour-location associations also induce contingent capture? To test this, we presented pre-cues in colours such as purple, which were completely task-irrelevant in that their colour had no association with space or target identification. While cues with location-associated colours continued to capture attention to their associated locations, no cues captured attention to their physical location, including task-irrelevant (purple) cues. Thus, attentional capture was contingent on cues matching one of the location-associated colours. These findings are consistent with a single attentional control process that determines both what types of stimuli capture spatial attention, and where attention goes in response to these stimuli.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".