Features are Also Important: Contributions of Featural and Configural Processing to Face Recognition
Why this work is in the frame
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Bibliographic record
Abstract
It has been suggested that face recognition is primarily based on configural information, with featural information playing little or no role. We investigated this idea by comparing the prototype effect for face prototypes that emphasized either featural or configural processing. In Experiment 1, participants showed a tendency to commit false alarms in response to nonstudied prototypes, and this tendency was equivalent for featural and configural prototypes. Experiment 2 replicated this finding, and provided support for the assumption that the two types of prototypes differed in terms of featural and configural processing: Face inversion eliminated the prototype effect for configural prototypes but not for featural prototypes. These results suggest that both featural and configural processing make important contributions to face recognition, and that their effects are dissociable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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