Emotion modulates cognitive flexibility in patients with major depression
Bibliographic record
Abstract
BACKGROUND: Depression is associated with alterations of emotional and cognitive processing, and executive control in particular. Previous research has shown that depressed patients are impaired in their ability to shift attention from one emotional category to another, but whether this shifting deficit is more evident on emotional relative to non-emotional cognitive control tasks remains unclear. METHOD: The performance of patients with major depressive disorder and matched healthy control participants was compared on neutral and emotional variants of a dynamic cognitive control task that requires participants to shift attention and response from one category to another. RESULTS: Relative to controls, depressed patients were impaired on both tasks, particularly in terms of performance accuracy. In the neutral go/no-go task, the ability of depressed patients to flexibly shift attention and response from one class of neutral stimuli to the other was unimpaired. This contrasted with findings for the emotional go/no-go task, where responding was slower specifically on blocks of trials that required participants to shift attention and response from one emotional category to the other. CONCLUSIONS: The present data indicate that any depression-related difficulties with cognitive flexibility and control may be particularly evident on matched tasks that require processing of relevant emotional, rather than simply neutral, stimuli. The implications of these findings for our developing understanding of cognitive and emotional control processes in depression are discussed.
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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".