A criterion-placement theory of face matching
Why this work is in the frame
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Bibliographic record
Abstract
Face matching is an important applied task that requires binary decisions to pairs of face images to determine whether these depict the same person (an identity match) or different people (a mismatch). While these choices are mutually exclusive, performance for match and mismatch trials appears to be dissociable, which poses a problem for theory development. The current study demonstrates that this dissociation arises from systematic response biases, which reflect individual differences in the placement of decision-making thresholds to distinguish matches from mismatches. When these biases are controlled or partialled out from classification accuracy, reliable associations between match and mismatch identifications are found. This is demonstrated over two experiments with a sample of over 500 participants, several face-matching tests, and a series of data simulations. These findings support a cognitive theory in which individual differences in the placement of decision-making thresholds provide the mechanism by which the identification of face matches and mismatches are linked.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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