Surprised but not scared: Similarities and differences in the perceptual structure of facial expressions of 7-year-olds and adults
Bibliographic record
Abstract
Children's ability to recognize facial expressions develops slowly through childhood. Pre-adolescent children are not as accurate as adults in recognizing intense facial expressions, especially when the expression is negative (e.g., fear and disgust, Durand et al., 2007). Studies using multidimensional scaling (MDS) indicate that adults' perception of facial expressions can be modeled by two underlying dimensions, namely, pleasure and arousal. In the current study, we used MDS to compare the perceptual structure of children and adults for facial expressions. When based only on intense expressions, the perceptual structure for facial expressions appears to already be adult-like in preschoolers (Russell & Bullock, 1985; 1986). Here, we collected similarity scores from a group of adults (n=16) using a rating paradigm and from another group of adults (n=16) and a group of 7-year-olds (n=16) using an “odd-one-out” paradigm for four different intensity levels of each of the facial expressions of six basic emotions. We calculated a three-dimensional solution using the INDSCAL procedure for each group (stress: 0.153 − 0.197), and applied hierarchical clustering analysis to the MDS solutions. Although tested with different paradigms, the two groups of adults had similar clustering. Similar to adults, children showed separated clusters for happiness and for anger, as well as a cluster including both sadness and disgust. Surprise and fear were clustered together in adults, but not in children. In adults, neutral was located close to happiness with all the lowest intensity expressions around it, while in children, neutral was close to surprise, and the lowest intensity expressions diverged from their main categories, but were not pulled toward neutral. The results suggest that 7-year-olds have a systematic perceptual structure of facial expressions that overlaps only partially with that of adults.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".