Spatial Frames of Reference of Attention in Three-Dimensional Space
Bibliographic record
Abstract
Spatial frames of reference in human attention have been well-studied for processing visual events in two-dimensional (2D) space, but not in three-dimensional (3D). Through the inhibition of return (IOR) effect in a modified spatial cueing paradigm, we have previously demonstrated a near-advantage in localization performance, and we identified that viewer-centered distance modulated spatial attention. In the current study, we examined the separate contributions of viewer-centered distance and world-centered depth on the IOR effect. We compared conditions where viewer-centered distance and world-centered depth could specify the same or different spatial relation between the cue and target. The results showed that IOR decreased in conditions with different cue/target world-centered depths but the same viewer-centered distance. However, IOR remained large when the cue/target stimuli appeared within the same world-centered depth, regardless of whether the cue/target held the same or different viewer-centered distance and was either within (Experiment 1) or beyond (Experiment 2) the same placeholder. The results suggested that when the target appears in a different world-centered depth from that of the cue, both viewer-centered distance and world-centered depth modulate spatial attention. However, when the target appears in the cued world-centered depth, inhibition is tagged to that depth plane in a winner-take-all manner.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".