Recognition of Deformed Familiar Faces: Contrast Negation and Nonglobal Stretching
Bibliographic record
Abstract
Familiar face recognition is robust to subtle and drastic changes in appearance. Knowing which conditions harm our recognition highlights underlying processes that have prominent roles in face learning. Here, we focused on two image deformations that studies suggest independently harm recognition: contrast negation and stretching of top or bottom halves of a face orthogonal to the unstretched half (nonglobal stretching). Participants were asked to categorize self-reported familiar or unfamiliar faces presented in photographic positive and negative in a fully within-subjects design. In Experiments 1 and 2, recognition of contrast-positive faces was robust to global and nonglobal stretching, suggesting iso-dimension ratios do not have a role in familiar face recognition. However, performance was consistently impaired by contrast negation in all configurational conditions. Further reductions in categorization accuracy when top halves of contrast-negated faces are stretched suggest some limited role for configuration under these image conditions. In Experiment 3, presenting the top or bottom half of nonglobal stretch conditions suggested categorization of nonglobal stretch faces did not require perception of the whole face, in the research design reported here. These results highlight further limits to configurational accounts of face recognition and indicate a relatively important role for surface reflectance cues.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".