Processing emotional facial expressions influences performance on a Go/NoGo task in pediatric anxiety and depression
Bibliographic record
Abstract
BACKGROUND: This study investigated whether processing emotionally salient information such as emotional facial expressions influences the performance on a cognitive control task in pediatric anxiety and depression. METHODS: The sample included 68 participants between 8 and 16 years of age selected into three diagnostic groups: Anxiety Disorder (ANX, n = 23), Major Depressive Disorder (MDD, n = 19), and Low-Risk Normal Control (LRNC, n = 26). Participants completed an Emotional Go/NoGo task in which participants must either respond to (Go trials) or not respond to (NoGo trials) specific facial expressions (angry, fearful, sad, happy, neutral). In order to manipulate the level of cognitive control needed to perform the task, the probability of occurrence of the Go trials was varied across 3 probability conditions (low, moderate, high). RESULTS: Analyses showed that the MDD group had significantly faster reaction times to sad face Go trials embedded in neutral face NoGo trials in the moderate probability condition and that the ANX group had significantly slower reaction times to neutral face Go trials embedded in angry face NoGo trials in the low probability condition. CONCLUSIONS: These data demonstrate that processing emotional facial expressions influences the performance on a cognitive control task in children and adolescents diagnosed with an anxiety disorder and major depression.
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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 | 0.003 |
| 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) | 0.001 | 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".