Perceived depth modulates allocation of attention
Bibliographic record
Abstract
There is growing evidence that the visual system processes objects perceived as closer more quickly and accurately than those farther away, a phenomenon known as the "close advantage effect" (CAE). The mechanisms underlying this effect, however, remain unknown. In this series of studies, we assessed whether perceived depth modulates allocation of attention. Using a Posner cueing task in Experiment 1a, we found greater accuracy for close surface targets (demonstrating the CAE). Critically, we also found a smaller Posner effect in the close surface, suggesting that attentional resources are better utilized in this space. Experiment 1b confirmed that these results were not due to differences in background surface sizes. In Experiment 2a, using the Egly-Driver task, we replicated and extended the results of Experiment 1a. Participants were more accurate when shifting attention from far to close surfaces and when shifting attention within the close surface, compared to when shifting attention within the far surface. These results suggest that perceived proximity makes attentional shifts more efficient. This effect persisted in Experiment 2b, even after controlling for the perceived size of the targets. Overall, our findings show that perceived depth modulates attention, with close space receiving preferential processing likely due to its relevance for immediate interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".