Bibliographic record
Abstract
This study involved two experiments. The goal of the first was to evaluate how visual attention is distributed spatially within an object, and how a spatial distribution may change over time. We accomplished this by having people press a button as soon as they noticed a target appear at various onset times and locations within an arch-shaped object. In the second experiment, we extended the arch-object and cued one end of it, in order to examine whether attention is biased to follow the shape of an object even if such a mechanism reduces the efficiency of a visual search. Results from the first experiment indicate that initially, there is no attentional bias to any location within an object. However, as looking time increases, a developing bias to the centre of objects occurs before attention adopts a strategic spatial distribution within the object. Results from the second experiment indicate that after attention is captured by a cued area, attention shifts away from the cued location. The path attention takes from the cued area is not constrained within the object. With increased time, however, attention does not move back to the cued location. Therefore, although attention is not constrained to follow the shape of the object one focuses on, it seems that the efficiency of a visual search is still jeopardized due to reluctance for attention to move to previously attended 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".