Spatial attention in three-dimensional space: A meta-analysis for the near advantage in target detection and localization
Bibliographic record
Abstract
Studies have explored how human spatial attention appears allocated in three-dimensional (3D) space. It has been demonstrated that target distance from the viewer can modulate performance in target detection and localization tasks: reaction times are shorter when targets appear nearer to the observer compared to farther distances (i.e., near advantage). Times have reached to quantitatively analyze this literature. In the current meta-analysis, 29 studies (n = 1260 participants) examined target detection and localization across 3-D space. Moderator analyses included: detection vs localization tasks, spatial cueing vs uncued tasks, control of retinal size across depth, central vs peripheral targets, real-space vs stereoscopic vs monocular depth environments, and inclusion of in-trial motion. The analyses revealed a near advantage for spatial attention that was affected by the moderating variables of controlling for retinal size across depth, the use of spatial cueing tasks, and the inclusion of in-trial motion. Overall, these results provide an up-to-date quantification of the effect of depth and provide insight into methodological differences in evaluating spatial attention.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".