Recognition and discrimination: Is there a role for context in face learning?
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
Recognizing unfamiliar identities in naturalistic images is challenging (e.g., two images of the same person are misperceived as belonging to different people). Face learning involves increased tolerance of variability in appearance and improved discrimination. I examined how a perceiver determines the range of inputs attributable to a newly learned identity, such that novel images of that identity are recognized, yet similar identities are excluded. I propose that learning a new face in the context of a similar identity facilitates learning via more precise representation. In two experiments, participants learned three identities (two similar, NNs; one dissimilar, FN) and were asked to recognize of two of those identities (one NN and FN). Performance did not vary for the NNs and FNs. Thus, identity learning involves both increased tolerance of variability and improved discrimination. We find no evidence that face learning is best accounted for by the multi-dimensional face space model.
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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 | 0.001 | 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 | 0.000 |
| 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