Biased Facial Expression Interpretation in Shy Children
Bibliographic record
Abstract
Abstract The relationship between shyness and the interpretations of the facial expressions of others was examined in a sample of 123 children aged 12 to 14 years. Participants viewed faces displaying happiness, fear, anger, disgust, sadness, surprise, as well as a neutral expression, presented on a computer screen. The children identified each expression by pressing a button on an external keyboard. For each expression, children also rated (a) the degree to which they felt the child displaying the expression would like them, (b) the probability that someone at school would look at them with that expression, and (c) their own emotional reaction to interacting with a child displaying the expression. Participants also completed the Children's Shyness Questionnaire and the Children's Rejection Sensitivity Questionnaire. We hypothesized that shyness in children would be related to negatively biased interpretations of facial expressions. Although the accuracy with which the children could identify the facial expressions was not related to their degree of shyness, negative biases were found in their interpretations of the meanings of the expressions. Furthermore, rejection sensitivity significantly mediated many of these biased interpretations. These findings may have implications for interventions for children experiencing shyness and social anxiety, especially social‐skills training approaches. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.007 |
| 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 | 0.001 |
| 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".