Revisiting upright and inverted face recognition in 6 to 12-year-old children and adults
Bibliographic record
Abstract
Adults are experts at recognizing faces. However there is still controversy about how this ability develops with age, with some arguing for adultlike processing by 4-6 years of age (Crookes & McKone, 2009) while others maintaining that this ability undergoes protracted development (Monldoch et al., 2002). Here we tested 108 6- to 12-year-old children and 36 young adults with a digitized version of the Benton Face Recognition Test (Benton et al., 1983), which is known to be a sensitive tool for assessing face recognition abilities (Busigny & Rossion, in press). Participants had to identify 3 faces among 6 alternatives that matched the target face despite changes in viewpoint and lightning. The faces were projected upright and upside-down in separate blocks, with order counterbalanced across participants. Children's correct response times did not improve with age, for either upright or inverted faces, but were significantly slower than those of adults for both conditions. This pattern is consistent with known increases with age in attention and information processing. Accuracy improved between 6 and 12 and significantly more for upright than inverted faces, leading to a larger face inversion effect in older children. Inverted face recognition improved slowly until late childhood whereas the improvement for upright faces was largest before versus after 8 years of age, with a further enhancement by young adulthood. Together, the results indicate that during childhood face processing becomes increasingly tuned to upright faces, likely as a result of increasing experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".