Crowding the face-space: The attractor field hypothesis and within-person variability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Face space theory suggests that faces that are similar to others (i.e., typical faces) are represented in denser regions in face space than distinctive faces. Accordingly, typical face representations can be activated by the same input, leading to mistakenly identifying a person as someone else. A modification of this theory can also accommodate the opposite error in which two images of the same person are mistaken for different people, which results from intolerance to within-person variability. In two experiments, we tested two predictions of the modified theory: (1) greater tolerance of within-person variability should be observed for distinctive faces and (2) the same conditions that increase tolerance to within-person variability should facilitate differentiation of two similar-looking identities. The results support the first of these predictions, but not the second. The findings are interpreted in the context of attention to differences vs. commonalities when learning to distinguish faces from similar-looking others.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it