Understanding Children’s Accuracy in Recognizing Facial Expressions of Pain
Bibliographic record
Abstract
Facial expressions of pain have an adaptive function in informing others of the need of attention and care. The detection of these nonverbal cues is particularly important in children since they are not always capable of expressing their needs verbally. Nevertheless, research recurrently shows that distinguishing between genuine, suppressed, and simulated pain expressions produced by children is a difficult task for adults; even when their professions require such a skill (e.g. doctors or nurses). Only a few studies have explored the development of this specific ability amongst children's peers. The current study aims to fill this literature gap by exploring children's ability to recognize and judge genuine, simulated, and suppressed expressions of pain produced by other children their age. Seventy-nine children from kindergarten to fourth grade viewed videos in which children encoders expressed the three aforementioned types of pain while plunging their hand in cold or warm water. Participants were asked to select the type of pain that was expressed. They were also asked their level of confidence in their answer and the level of pain they thought the children were experiencing. Despite having a high level of confidence in their answers, kindergarteners had a significantly lower proportion of correct answers compared to children in third and fourth grade. Furthermore, regardless of their grade level, children were better at recognizing suppressed pain expressions and had lower performance rates for genuine pain recognition. Our overall findings revealed an improvement in children's performance with aging.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".