Bibliographic record
Abstract
Occluders are prevalent in the visual environment. For example, we need to see through the rain drops on the windshield when driving, or see through the blinds from our living room. Some occluders are random (e.g., rain drops), while others are structured (e.g., blinds). How does occluder pattern influence visual perception? To answer this question, we presented occluded scenes to participants and asked them to judge whether the scene was indoors or outdoors in Experiment 1. The pattern of the occluders was either random or structured (e.g., stripy). The occluders were either chunky (leaving large chunks of the scene visible), or fine-grained (similar to mesh). Scene discrimination was better when the occluders were structured than random but only for fine-grained occluders, and it was identical for structured and random occluders when they were chunky. This suggests that fine-grained structured occluders are more visually penetrable than random occluders. For chunky occluders, the need for visual penetration is reduced due to the large intact scene chunks, which could explain the lack of difference. To see whether this effect is specific to scene perception, we replicated the experiment using object images where participants viewed occluded objects and judged whether the object was animate or inanimate in Experiment 2. The results were highly similar: object discrimination was better when fine-grained occluders were structured than random, but performance was comparable for chunky structured and random occluders. Finally, we extended our findings to numerosity perception where participants estimated the number of objects in an occluded array in Experiment 3. Number estimation was more accurate when fine-grained occluders were structured than random. Therefore, for all three types of perception (scene, object, number) fine-grained structured occluders are more visually penetrable than random occluders. The findings reveal new insights into how visual perception operates with incomplete information.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".