Impaired Facial Affect Perception in Unaffected Children at Familial Risk for Panic Disorder
Bibliographic record
Abstract
Recent studies suggest that impaired processing of facial affect has a familial component and may reflect a marker of liability to psychopathology. This study investigated whether facial affect processing is impaired in offspring with parental panic disorder (PD). Psychiatrically healthy children with parental PD (n = 51) and age and sex matched control children with no parental psychopathology (n = 51) completed a standard facial recognition task. High-risk children made more errors recognizing fearful faces than controls and misattributed fear and angry facial affect as surprised. High-risk females also made more errors recognizing sad faces compared to low risk females and misattributed sadness as fear. No difference emerged for self-rated anxiety while viewing facial expressions. However, self-rated anxiety correlated moderately with misrecognition of fearful facial affect in high-risk children. Overall, our data suggest that the ability to correctly recognize negative facial emotions is impaired in children with parental PD. Further research is needed to confirm if these deficits represent a trait marker of liability for PD and elucidate the contribution of genetic and family environmental influences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".