Perceived distance modulates attention allocation
Bibliographic record
Abstract
Recent experiments suggest that objects perceived to be near our body are processed more quickly and precisely than those that appear farther away. While it is clear this phenomenon, (appropriately termed the “close advantage effect”) occurs, the source of this improved processing remains an open question. One possibility is that attention is allocated more readily to closer objects than those that seem farther away. To examine this hypothesis, we evaluated whether attention orientation is modulated by perceived depth. As in previous experiments, depth differences were created using versions of the Ponzo illusion. In Experiment 1, we used a Posner cuing task where participants detected a target after receiving valid or invalid spatial cues to its location. Critically, on each trial the possible target locations (and cue) were confined to be either on the close or far surface of the illusion. We found greater accuracy for targets on the close surface (consistent with previous studies), and an advantage for the cued location. Notably, the positive effect of the cue was similar for close and far surfaces, suggesting that attention allocation within each surface was similar. In Experiment 2, we used the Egly-Driver task to explore whether attention operates differently when moving between close and far surfaces. In this case, cues and targets appeared on the same surface or different surfaces (valid or invalid). We found an interaction between cue validity and location of the target, such that the close advantage effect was greater for invalid trials. This suggests that observers had more difficulty disengaging attention from the apparently closer surface, even though the two regions were on the same physical plane. Taken together, our results suggest that there is privileged allocation of attention for objects perceived as closer, particularly when attention is split across distance.
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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 | 0.000 | 0.006 |
| 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) | 0.003 | 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".