Fitting the child's mind to the world: adaptive norm‐based coding of facial identity in 8‐year‐olds
Bibliographic record
Abstract
In adults, facial identity is coded by opponent processes relative to an average face or norm, as evidenced by the face identity aftereffect: adapting to a face biases perception towards the opposite identity, so that a previously neutral face (e.g. the average) resembles the identity of the computationally opposite face. We investigated whether children as young as 8 use adaptive norm-based coding to represent faces, a question of interest because 8-year-olds are less accurate than adults at recognizing faces and do not show the adult neural markers of face expertise. We found comparable face identity aftereffects in 8-year-olds and adults: perception of identity in both groups shifted in the direction predicted by norm-based coding. This finding suggests that, by 8 years of age, the adaptive computational mechanisms used to code facial identity are like those of adults and hence that children's immaturities in face processing arise from another source.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".