Emotional reactivity and perspective-taking in individuals with and without severe depressive symptoms
Bibliographic record
Abstract
The perspective-taking ability to imagine another person's feelings and thoughts is paramount for successful communication. This study pursued two questions regarding the link between perspective-taking and depressive symptomatology in a task where participants provided responses to words ranging in their positivity. First, we examined in a between-participants experimental manipulation how the presence of depressive symptoms influenced participants' emotional reactivity. Second, we measured within-participants, how their responses change as a function of the perspective they are assigned to take, that of a depressed or a non-depressed person. Our main interest is in the interaction of the two effects: we examine how one's emotional state determines the ability to engender someone else's responses. Our central finding is that depressive symptoms lead to emotional insensitivity, i.e., weaker responses to extremely positive and negative words. Furthermore, depressive symptoms come with a much weaker ability to take a non-depressed perspective. Finally, non-depressed participants demonstrated an excellent ability to mimic the blunt affect of depression when responding for the other group, suggesting that the outlook of a depressed individual is available to people throughout the range of depressive symptomatology. We discuss the implications of these findings for quantifying emotional reactivity during depression, as well as the diagnosis and prognosis of 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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".