The development of face Prototypes: evidence for simple and opposing aftereffects in children
Bibliographic record
Abstract
Norm-based coding underlies adults' expert face processing (Valentine, 1991). Adaptation aftereffects for several facial characteristics (e.g., race, sex) indicate that this prototype is updated as new faces are encountered (Webster et al., 2004). For example, prolonged exposure (adaptation) to one kind of facial distortion (e.g. facial features compressed inward) temporarily shifts preferences, making similarly distorted faces appear more attractive. Adults‘ face space has been further specified by opposing aftereffects: When adapted to two face categories (e.g. Caucasian and Chinese) distorted in opposite directions (e.g. expanded vs. compressed), adults‘ attractiveness ratings shift in opposite directions (Jaquet et al., 2007), as long as the two sets of faces belong to different categories (Bestelmeyer et al., 2008). Recent studies have used aftereffects as a tool to investigate the development of expert face processing. Our lab has shown that 8-year-olds exhibit attractiveness aftereffects in the context of a computerized storybook (Anzures, et al., in press). Here we extend our previous work in two ways. First, using a slightly modified method we provide the first demonstration of attractiveness aftereffects in 5-year-old children. After reading a storybook with either compressed or expanded facial features, 5-year-olds were more likely to choose a face distorted in the direction of adaptation than an undistorted face when asked which member of a face pair was more attractive, ps p = .02. For example, following adaptation to compressed Chinese and expanded Caucasian faces, 8-year-olds' attractiveness ratings selectively increased for compressed Chinese and expanded Caucasian faces. We are currently testing 5-year-old children for opposing after-effects.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".