Impaired Central Processing of Emotional Faces in Anorexia Nervosa
Bibliographic record
Abstract
Objectives: To elucidate the potential relationship between classification of emotional faces and impaired central processing in eating disorders and to investigate the potential mediatory role of alexithymia and depression in this relationship. Methods: Visual-evoked potentials (VEPs) to emotional faces and classification performance were assessed in 12 anorexic females and matched healthy controls. Results: Patients with anorexia nervosa showed no modulation of emotional face processing and displayed significantly increased N200 amplitudes in response to all emotional categories and decreased VEPs in response to unpleasant emotional faces in the P300 time range as compared with healthy controls. They also made more mistakes in emotional face recognition, in particular, for neutral, sad, and disgusted content. Conclusions: There are marked differences in evoked potentials and emotion recognition performances of patients with anorexia nervosa and controls in facial processing. Differences in brain dynamics might contribute to difficulties in the correct recognition of facially expressed emotions, deficits in social functioning, and in turn the maintenance of eating disorders. ED = eating disorder; ERP = event-related potential; VEP = visual-evoked potential; AN = anorexia nervosa; MDD = major depressive disorder; EEG = electroencephalography; TAS = Toronto Alexithymia Scale; BMI = body mass index; BDI = Beck Depression Inventory; STAI = State Trait Anxiety Inventory.
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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 | 0.000 | 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) | 0.002 | 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".