Configural Face Processing Develops more Slowly than Featural Face Processing
Bibliographic record
Abstract
Expertise in face processing takes many years to develop. To determine the contribution of different face-processing skills to this slow development, we altered a single face so as to create sets of faces designed to measure featural, configural, and contour processing. Within each set, faces differed only in the shape of the eyes and mouth (featural set), only in the spacing of the eyes and mouth (spacing set), or only in the shape of the external contour (contour set). We presented adults, and children aged 6, 8, and 10 years, with pairs of upright and inverted faces and instructed them to indicate whether the two faces were the same or different. Adults showed a larger inversion effect for the spacing set than for the featural and external contour sets, confirming that the spacing set taps configural processing. On the spacing set, all groups of children made more errors than adults. In contrast, on the external contour and featural sets, children at all ages were almost as accurate as adults, with no significant difference beginning at age 6 on the external contour set and beginning at age 10 on the featural set. Overall, the results indicate that adult expertise in configural processing is especially slow to develop.
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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) | 0.001 | 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 | 0.000 | 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".