Proneness to Decreased Negative Emotions in Major Depressive Disorder when Blaming Others rather than Oneself
Bibliographic record
Abstract
BACKGROUND: One widespread view holds that vulnerability to major depressive disorder (MDD) is linked to overall increases in negative emotionality. In contrast, cognitive attribution theories emphasize the importance of blaming oneself rather than others for negative events. Thus far, the contrasting predictions of these models have not been directly compared. Following the attributional perspective, we tested the hypothesis that people with remitted MDD show no overall bias towards negative emotions, but a selective bias towards self-blaming emotions relative to those emotions associated with blaming others. SAMPLING AND METHODS: We compared a remitted MDD and a control group on a novel experimental test that allowed us to directly compare proneness to specific emotions associated with different types of self-blame (guilt, shame, self-contempt/disgust) and blame of others (other-indignation/anger, other-contempt/disgust) whilst controlling for negative valence and medication status, and excluding comorbidity. RESULTS: In agreement with our hypothesis, individuals with remitted MDD exhibited an increased self-contempt bias (difference between contempt/disgust towards self and others) but no increased proneness to any other negative emotion or overall increases in perceived negative valence of stimuli. Moreover, the remitted MDD group exhibited reduced contempt/disgust towards others. CONCLUSIONS: Our results corroborate the prediction that vulnerability to MDD is associated with an imbalance of specific self- and other-blaming emotions rather than a general increase in negative emotions. Based on the composition of our sample, we speculate that self-contempt bias may be particularly characteristic of melancholic MDD subtypes and could be useful for stratification of depression in the future.
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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 | 0.002 |
| 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".