Misperceptions of Facial Emotions Among Youth Aged 9–14 Years Who Present Multiple Antecedents of Schizophrenia
Bibliographic record
Abstract
Similar to adults with schizophrenia, youth at high risk for developing schizophrenia present difficulties in recognizing emotions in faces. These difficulties might index vulnerability for schizophrenia and play a role in the development of the illness. Facial emotion recognition (FER) impairments have been implicated in declining social functioning during the prodromal phase of illness and are thus a potential target for early intervention efforts. This study examined 9- to 14-year-old children: 34 children who presented a triad of well-replicated antecedents of schizophrenia (ASz), including motor and/or speech delays, clinically relevant internalizing and/or externalizing problems, and psychotic-like experiences (PLEs), and 34 typically developing (TD) children who presented none of these antecedents. An established FER task (ER40) was used to assess correct recognition of happy, sad, angry, fearful, and neutral expressions, and facial emotion misperception responses were made for each emotion type. Relative to TD children, ASz children presented an overall impairment in FER. Further, ASz children misattributed neutral expressions to face displaying other emotions and also more often mislabeled a neutral expression as sad compared with healthy peers. The inability to accurately discriminate subtle differences in facial emotion and the misinterpretation of neutral expressions as sad may contribute to the initiation and/or persistence of PLEs. Interventions that are effective in teaching adults to recognize emotions in faces could potentially benefit children presenting with antecedents of schizophrenia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".