Impaired fixation to eyes during facial emotion labelling in children with bipolar disorder or severe mood dysregulation
Bibliographic record
Abstract
BACKGROUND: Children with bipolar disorder (BD) or severe mood dysregulation (SMD) show behavioural and neural deficits during facial emotion processing. In those with other psychiatric disorders, such deficits have been associated with reduced attention to eye regions while looking at faces. METHODS: We examined gaze fixation patterns during a facial emotion labelling task among children with pediatric BD and SMD and among healthy controls. Participants viewed facial expressions with varying emotions (anger, fear, sadness, happiness, neutral) and emotional levels (60%, 80%, 100%) and labelled emotional expressions. RESULTS: Our study included 22 children with BD, 28 with SMD and 22 controls. Across all facial emotions, children with BD and SMD made more labelling errors than controls. Compared with controls, children with BD spent less time looking at eyes and made fewer eye fixations across emotional expressions. Gaze patterns in children with SMD tended to fall between those of children with BD and controls, although they did not differ significantly from either of these groups on most measures. Decreased fixations to eyes correlated with lower labelling accuracy in children with BD, but not in those with SMD or in controls. LIMITATIONS: Most children with BD were medicated, which precluded our ability to evaluate medication effects on gaze patterns. CONCLUSION: Facial emotion labelling deficits in children with BD are associated with impaired attention to eyes. Future research should examine whether impaired attention to eyes is associated with neural dysfunction. Eye gaze deficits in children with BD during facial emotion labelling may also have treatment implications. Finally, children with SMD exhibited decreased attention to eyes to a lesser extent than those with BD, and these equivocal findings are worthy of further study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".