Ensemble perception of faces with naturalistic occlusions
Bibliographic record
Abstract
The visual system takes advantage of redundancy in the world by extracting summary statistics, a phenomenon known as ensemble perception. Ensemble representations are formed for low-level features like orientation and size and high-level features such as facial identity and expression. Whereas recent research has shown that the visual system forms intact ensemble representations even when faces are partially occluded via solid bars, how ensemble perception is impacted with the addition of naturalistic objects such as face masks or sunglasses is largely unknown. To investigate this, we conducted a series of experiments using continuous report tasks in which faces (either varying in identity or expression) were partially occluded with a surgical mask or sunglasses and participants had to report the average face using a face wheel. We found evidence that participants could still accurately extract the average even when a significant portion of it was occluded with either face masks or sunglasses. In a second experiment, however, we found performance was worse when the face wheel was variable trial to trial. Thus part of the preservation of performance in occlusion arises from the visual system learning the features of the particular face wheel being used. Overall, our results suggest that the visual system is able to establish robust ensemble representations for faces with naturalistic occlusions, but that robustness appears to be supported at least partially by learning information about the particular features that are informative for a given set of faces.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".