Associations of Internalizing and Externalizing Problems with Facial Expression Recognition in Preschoolers: The Generation <scp>R</scp> Study
Bibliographic record
Abstract
Abstract Altered patterns of facial expression recognition ( FER ) have been linked to internalizing and externalizing problems in school children and adolescents. In a large sample of preschoolers ( N = 727), we explored concurrent and prospective associations between internalizing/externalizing problems and FER . Internalizing/externalizing problems were rated by parents at 18 and 36 months using the Child Behavior Checklist. FER was assessed at 36 months using age‐appropriate computer tasks of emotion matching and emotion labeling. Internalizing problems were associated with emotion‐specific differences at both ages: at 18 months they predicted more accurate labeling of sadness; at 36 months they were associated with less accurate labeling of happiness and anger. Externalizing problems at both ages were associated with general FER deficits, particularly for matching emotions. Findings suggest that in preschoolers, internalizing problems contribute to emotion‐specific differences in FER , while externalizing problems are associated with more general FER deficits. Knowledge of the specific FER patterns associated with internalizing/externalizing problems can be proven useful in the refinement of emotion‐centered preventive interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".