Theory of Mind, Excessive Reassurance-Seeking, and Stress Generation in Depression: A Social-Cognitive-Interpersonal Integration
Bibliographic record
Abstract
Introduction: According to the interpersonal model of depression, individuals with depression engage in excessive reassurance-seeking (ERS) about others’ beliefs regarding their self-worth, which can ultimately result in interpersonal rejection. We present the novel hypothesis that maladaptive ERS behaviors in depression may be driven by difficulties with “theory of mind”—the foundational ability to decode and reason about others’ mental states. Method: Participants included 31 young adults in a current episode of a unipolar depressive disorder, and 91 never-depressed adults. Theory of mind was assessed with standard, objective laboratory tasks. Stressful life events were assessed with a gold-standard contextual interview and independent rating system. Results: Consistent with hypotheses, in the depressed group only, lower accuracy of theory of mind decoding was associated with greater ERS, which was significantly associated with exposure to greater interpersonal, but not non-interpersonal, stress. Surprisingly, higher accuracy of theory of mind reasoning was associated with greater ERS. Discussion: The intriguing dissociation is discussed in terms of expanding the interpersonal model of depression to include the foundational social cognitive processes that underlie effective social communication.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".