How Does Reflexive Visuospatial Attention Speed Target Processing?
Bibliographic record
Abstract
Irrelevant transient stimuli can speed responses to visual targets that appear soon after at the same location (relative to other locations). How do these stimuli speed target processing? Traditionally, they are thought to act as cues that reflexively capture visuospatial attention, a mechanism that provides processing priority to specific regions of the visual field. Here we report behavioral and electrophysiological evidence of the limits of this explanation. In the first experiment we show that while targets are identified faster at a cues locations (the classic cueing effect), this effect is increased when the cue and target are visually similar. Thus, the reflexive cueing effect is not a general attentional enhancement of all visual processing within a region of space; rather, some component of the effect is related to the identity of the cue. In a second experiment we used attentional control settings to manipulate whether cues captured attention or not and measured event-related potentials. Cues that captured attention produced a posterior contralateral positivity between 200 to 400 ms after their onset that was absent when they did not capture attention. This component resembles the Ptc, which has been associated with the resolution of perceptual competition between proximal stimuli. More importantly, a similar component was observed time-locked to the target onset, except when the target appeared at a cued location. Thus cues may speed target processing by inducing competition resolution, making this process unnecessary when the target subsequently appears at that location. These results do not fit well with the notion that reflexive attention is a mechanism deployed to enhance visual processing within regions of space. Instead, the present results suggest that transient stimuli initiate perceptual processing, and subsequent targets can exploit these ongoing processes if, for example, they appear at the same location or are visually similar.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".