Face Context Advantage Explained by Vernier and Separation Discrimination Acuity
Bibliographic record
Abstract
Seeing facial features in the context of a full face is known to provide an advantage for perception. Using an interocular separation perception task we confirmed that seeing eyes within the context of a face improves discrimination in synthetic faces. We also show that this improvement of the face context can be explained using the presence of individual components of the face such as the nose mouth, or head-outline. We demonstrate that improvements due to the presence of the nose, and head-outline can be explained in terms of two-point separation measurements, obeying Weber's law as established in the literature. We also demonstrate that performance improvements due to the presence of the mouth can be explained in terms of Vernier acuity judgments between eye positions and the corners of the mouth. Overall, our study shows that the improvements in perception of facial features due to the face context effect can be traced to well understood basic visual measurements that may play a very general role in perceptual measurements of distance. Deficiencies in these measurements may also play a role in prosopagnosia. Additionally, we show interference of the eyebrows with the face-inversion effect for interocular discrimination.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".