The importance of internal and external features in recognizing faces that vary in familiarity and race
Bibliographic record
Abstract
Familiar and unfamiliar faces are recognized in fundamentally different ways. One way in which recognition differs is in terms of the features that facilitate recognition: previous studies have shown that familiar face recognition depends more on internal facial features (i.e., eyes, nose and mouth), whereas unfamiliar face recognition depends more on external facial features (i.e., hair, ears and contour). However, very few studies have examined the recognition of faces that vary in both familiarity and race, and the reliance on different facial features, whilst also using faces that incorporate natural within-person variability. In the current study, we used an online version of the card sorting task to assess adults’ ( n = 258) recognition of faces that varied in familiarity and race when presented with either the whole face, internal features only, or external features only. Adults better recognized familiar faces than unfamiliar faces in both the whole face and the internal features only conditions, but not in the external features only condition. Reasons why adults did not show an own-race advantage in recognition are discussed.
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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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".