I guess I just have one of those faces: The effect of similar intervening identities on familiarization
Bibliographic record
Abstract
People can become familiar with a target identity from different photos of the target interspersed among intervening distractor identities. We investigated whether the degree of similarity between a target face and these intervening distractors influences familiarization. Face space theory makes the clear prediction that similar identities would be encoded closely together in face space, creating interference from similar faces that hinders familiarization. In contrast, recent work showing an important role for idiosyncratic variability in identity learning suggests that increasing the similarity of irrelevant intervening distractors should encourage viewers to attend to the target’s features that are most relevant for distinguishing it from the distractors, leading to a more refined and precise representation that would facilitate familiarization. Observers were trained with multiple photographs of a target identity presented among encounters with distractor identities that were morphed with the target face in varying percentages to achieve either high, medium, or low similarity to the target. Upon completing the training session, observers were given a matching task to test their familiarization with the target. Preliminary results revealed that accuracy in the matching task decreased as the similarity between the target and the intervening identities increased, providing support for the face space theory. Our results suggest that when learning a newly encountered target face, training with intervening distractors that highly resemble the target hinders the familiarization process.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".