Happy or Sad? Context Does Not Influence Four-Year-Olds' Perception of Happy and Sad Facial Expressions
Bibliographic record
Abstract
Adults' perception of facial displays of emotion is influenced by context; they make more errors and take longer to respond when the emotion displayed by the face (e.g., fear) is incongruent with the emotion displayed by the body (e.g., anger) (Meeren et al., 2005). This congruency effect is larger when emotions are similar (Aviezer et al., 2008). Previously, we reported that, like adults, 8-year-olds show congruency effects for sad/fearful facial expressions, but not sad/happy facial expressions, perhaps because happy and sad expressions differ in valence and intensity whereas fearful and sad expressions differ only in intensity (Widen & Russell, 2008). Additionally, happy and sad expressions share fewer physical characteristics than fearful and sad expressions (Aviezer et al., 2008). Because even infants and pre-school children are sensitive to emotional valence (Widen & Russell, 2008) we hypothesized that pre-school children would not show congruency effects for happy and sad facial expressions. Four-year-old participants (n = 12) helped a puppet sort faces into one of two houses (a happy or sad house); faces were presented in isolation, and on congruent/incongruent bodies. They showed no effect of congruency, p > .20, despite sorting isolated bodies almost perfectly. In a control study designed to verify that the lack of congruency effect could not be attributed to our child-friendly method, adults (n = 12) performed this same task with face/body expressions previously shown to elicit a congruency effect - sad/fear. They made more errors on incongruent trials, p < .01. Our data suggest that children's early sensitivity to emotional valence precludes congruency effects for happy/sad expressions, which we are testing in a follow-up study with two to three-year-old children - the youngest children that can reliably sort these facial expressions. Collectively, these results provide novel insights about how and when context influences children's sensitivity to facial expressions.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".