Effect of background texture on target detection: masking or differential processing for near and far pictorial depth?
Bibliographic record
Abstract
It is well established that the 2D spatial extent of selective attention is limited, as described by the useful field of view (UFOV), the 2D spatial extent of the visual field from which information can be extracted without eye or head movements. However, less is known about how selective attention varies in 3D. We (Song et al., VSS 2017) previously investigated the horizontal extent of visual attention while simulating depth through pictorial and optical flow cues in a driving scenario. In our modified UFOV task, participants showed better detectability for brief peripheral targets at a near depth compared to a far depth. Although we ensured that the retinal size of the targets was identical across both depths, the retinal characteristics of the textured, checker backgrounds on which targets appeared differed across conditions. Specifically, backgrounds in the far condition extended over a smaller area and consisted of smaller checkerboards than backgrounds in the near condition. Thus, the depth effect we found previously may have been due to increased masking effects of the background at greater simulated depths, rather than differences in perceived depth per se. The current study examined this hypothesis by measuring the effect of check size and background extent on the detectability of brief peripheral targets presented at a single depth. Results from 13 observers suggest that check size and background extent have very small effects on the detectability of peripheral targets in our conditions. Hence, it is unlikely that the effect of depth reported by Song et al. can be accounted for by differential masking by the target background in the near and far conditions, supporting the idea that attention covers a greater extent for near than for far targets. We are continuing this line of investigation by examining the effect of the ground texture on target detection. Meeting abstract presented at VSS 2018
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".