Discrimination of Facial Features by Adults, 10-Year-Olds, and Cataract-Reversal Patients
Bibliographic record
Abstract
In previous studies we created 8 new versions of a single face: 4 differed only in the spacing among features and 4 differed in the shape of the eyes and mouth. Compared to the spacing set, results for this feature set indicated little impairment by inversion, earlier adult-like accuracy (Mondloch et al, 2002 Perception 31 553-566), and normal performance after a history of early visual deprivation from bilateral congenital cataract (Le Grand et al, 2001 Nature 410 890, 412 786). Here we addressed the possibility that this pattern might have resulted from our having inadvertently selected easily discriminated features or including some faces with make-up. We created 20 featural versions of a single female face and asked adults, 10-year-old children, and patients treated for bilateral congenital cataract to make same/different judgments for 120 pairings (half different). The results confirm that adults easily discriminate facial features, even after early visual deprivation from cataract, and that inversion has only a small effect. By the age of 10 years, children are close to, but not quite at, adult levels of accuracy. The previous findings cannot be attributed to our having inadvertently created a feature set that was unusually easy to discriminate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".