Much ado about nothing: Capturing attention toward locations without new perceptual events.
Bibliographic record
Abstract
Popular frameworks of attention propose that visual orienting occurs through a combination of bottom-up (stimulus-driven) and top-down (goal-directed) processes. Much of the basic research on these processes adheres paradigmatically to experimental methods that introduce salient but task-irrelevant stimuli (objects or transients) to the visual environment to determine whether attention is captured to their locations. This common practice of changing or adding a stimulus to a location to determine whether it captures attention reflects a notion that locations at which new features or stimuli spontaneously appear are prioritized above all else. In this article, we challenge this notion with results from a modified additional singleton paradigm. In the critical condition, following a preview array of placeholder stimuli, 1 placeholder stimulus transforms into a target diamond and changes luminance at the same time that all other placeholders, except 1 (the truly "static singleton") change in luminance. This static singleton location, which involves neither a new stimulus nor any sensory transient, produces a clear pattern of attentional capture originating near its location. These findings violate multiple bottom-up and top-down perspectives while encouraging a new approach to studying attentional capture. (PsycINFO Database Record
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".