The effect of apparent distance on peripheral target detection
Bibliographic record
Abstract
Previous research suggests that peripheral target detection is modulated by viewing distance and distance simulated by pictorial cues and optic flow. In the latter case, it is unclear what cues contribute to the effect of distance. The current study evaluated the effect of distance on peripheral detection in a virtual three-dimensional environment. Experiments 1-3 used a continuous, dynamic central task that simulated observers traveling either actively or passively through a virtual environment following a car. Peripheral targets were flashed on checkerboard-covered walls to the left and right of the path of motion, at a near and a far distance from the observer. The retinal characteristics of the targets were identical across distances. Experiment 1 found more accurate and faster detection for near targets compared to far targets, especially for larger eccentricities. Experiment 2 equated the predictability of target onset across distances and found the near advantage for larger eccentricities in accuracy but a much smaller effect in reaction time (RT). Experiment 3 removed the checkerboard background implemented in Experiments 1 and 2, and Experiment 4 manipulated several static, monocular cues. Experiments 3 and 4 found that the variation in the density of the checkerboard backgrounds could explain the main effect of distance on accuracy but could not completely account for the interaction between target distance and eccentricity. These results suggest that attention is modulated by target distance, but the effect is small. Finally, there were consistent divided attention costs in the central car-following task but not the peripheral detection task.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".