Face inversion and contrast‐reversal effects across development: in contrast to the expertise theory
Bibliographic record
Abstract
To determine the role of configural changes on the development of face encoding and memory, we investigated face recognition in an n-back repetition task with upright, inverted and contrast-reversed unfamiliar faces in adults and children (8-16 years). Repetitions occurred immediately (0-lag) or after one intervening face (1-lag). Face recognition continued to develop beyond 14-16 years, as shown with hit rates, d' scores and reaction times that all improved with age. Inversion and contrast-reversal effects were found in all subjects but were not more pronounced with increasing age, suggesting no increased reliance on configural processing and thus arguing against the expertise theory of Diamond and Carey (1986). Recognition improved with age in upright but also in inverted and contrast-reversed faces, suggesting a quantitative rather than a qualitative developmental change in face processing. For all age groups, performances decreased and reaction times increased from 0- to 1-lag conditions similarly, suggesting a similar memory component involved in adults' and children's processing. These data suggest gradual quantitative improvements in face processing with age, mainly due to increasing working memory processing capacity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".