COVID-19 and Protective Equipment: Impact of the Obstruction of Facial Features on the Recognition of Emotional Facial Expressions in Children
Bibliographic record
Abstract
The arrival of COVID-19 came along with the requirement of wearing protective gear, like facial masks, which potentially affects the recognition of facial expressions in others. The aim of the current study was to examine the impact of a facial feature obstruction on emotional facial expression recognition of the six basic emotions in children aged 5 and 10 years old. Children were presented 24 stimuli within two different conditions: the full face and only the eyes visible. While both age groups were similarly affected by reduced facial information, differences emerged depending on the emotion: 5-year-olds struggled more with recognizing happiness from just the eyes, whereas 10-year-olds had more difficulty with anger. Nevertheless, both age groups had a significant reduction in accuracy for most emotions (4 out 6). Common confusions, such as mistaking fear for surprise or disgust for anger, might have contributed to the variations in results from previous studies.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".