Tuning to the Positive: Age-Related Differences in Subjective Perception of Facial Emotion
Bibliographic record
Abstract
Facial expressions aid social transactions and serve as socialization tools, with smiles signaling approval and reward, and angry faces signaling disapproval and punishment. The present study examined whether the subjective experience of positive vs. negative facial expressions differs between children and adults. Specifically, we examined age-related differences in biases toward happy and angry facial expressions. Young children (5-7 years) and young adults (18-29 years) rated the intensity of happy and angry expressions as well as levels of experienced arousal. Results showed that young children-but not young adults-rated happy facial expressions as both more intense and arousing than angry faces. This finding, which we replicated in two independent samples, was not due to differences in the ability to identify facial expressions, and suggests that children are more tuned to information in positive expressions. Together these studies provide evidence that children see unambiguous adult emotional expressions through rose-colored glasses, and suggest that what is emotionally relevant can shift with development.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".