Improving identity matching of newly encountered faces: Effects of multi-image training.
Why this work is in the frame
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Bibliographic record
Abstract
Humans are error-prone at matching identity in photos of unfamiliar faces, especially in ambient images that incorporate natural variability in appearance. Nonetheless, matching faces to photographs is heavily relied upon in applied settings (e.g., when crossing the border). Whereas past training protocols emphasized discriminating highly similar identities, we incorporated within-person variability in appearance during training and in our identity-matching task. On each of five training days, participants learned six images per each of six identities. Accuracy improved on an identity-matching task for new images of trained identities, with no generalization to different identities. Experiment 1b suggests that learning multiple images of each identity was key; we found no significant improvement when training involved learning a single image of 12 identities. Collectively, our results have implications for understanding the process of face learning and improving recognition in applied settings.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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