The development of template-based facial expression perception from 6 to 15 years of age.
Bibliographic record
Abstract
When perceiving emotional facial expressions, adults use a template-matching strategy, comparing the perceived face with a stored representation. A rejection of unnaturally exaggerated faces is characteristic of this strategy because the exaggerated expressions do not match the stored template. In contrast, a rule-based perceptual strategy (e.g., wide eyes indicate surprise) would be more tolerant of exaggeration. The current study uses exaggeration tolerance to test the expression perception strategies of children from 6 to 15 years of age. In Experiment 1, 62 (38 male) participants viewed pairs of happy or sad faces varying in exaggeration and selected the face that looked closest to how a happy (or sad) person really looks. With age, children became less likely to choose the more exaggerated expression. In Experiment 2, this result was replicated with each of the six basic emotions. Sixty-six children (26 male, 50 Caucasian, 10 mixed-race, four Indian, two unidentified) from 6 to 15 years of age completed the same experimental tasks as Experiment 1 for all six emotions. Again, with age children became less likely to choose the more exaggerated face. The results from both experiments suggest that the development of an adult-like template-matching strategy lasts into adolescence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".