Facial emotion recognition in children treated for posterior fossa tumours and typically developing children: A divergence of predictors
Bibliographic record
Abstract
Facial emotion recognition (FER) deficits are evident and pervasive across neurodevelopmental, psychiatric, and acquired brain disorders in children, including children treated for brain tumours. Such deficits are thought to perpetuate challenges with social relationships and decrease quality of life. The present study combined eye-tracking, neuroimaging and cognitive assessments to evaluate if visual attention, brain structure, and general cognitive function contribute to FER in children treated for posterior fossa (PF) tumours (patients: n = 36) and typically developing children (controls: n = 18). To assess FER, all participants completed the Diagnostic Analysis of Nonverbal Accuracy (DANVA2), a computerized task that measures FER using photographs, while their eye-movements were recorded. Patients made more FER errors than controls (p < .01). Although we detected subtle deficits in visual attention and general cognitive function in patients, we found no associations with FER. Compared to controls, patients had evidence of white matter (WM) damage, (i.e., lower fractional anisotropy [FA] and higher radial diffusivity [RD]), in multiple regions throughout the brain (all p < .05), but not in specific WM tracts associated with FER. Despite the distributed WM differences between groups, WM predicted FER in controls only. In patients, factors associated with their disease and treatment predicted FER. Our study provides insight into predictors of FER that may be unique to children treated for PF tumours, and highlights a divergence in associations between brain structure and behavioural outcomes in clinical and typically developing populations; a concept that may be broadly applicable to other neurodevelopmental and clinical populations that experience FER deficits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".