Dissociating and linking divergent effects of emotion on cognition: insights from current research and emerging directions
Bibliographic record
Abstract
This century has witnessed unprecedented increasing interest in the investigation of emotion-cognition interactions and the associated neural mechanisms. The present review emphasizes the need to consider the various factors that can influence enhancing and impairing effects of emotion on cognition, in studies of both healthy and clinical groups. First, we discuss advances in understanding the circumstances in which emotion enhances or impairs cognition at different levels, both within the same processes (e.g., perception, episodic memory) and across different processes (i.e., episodic vs. working memory). Then, we discuss evidence regarding these opposing effects of emotion in a larger context, of the response to stressors, and linked to the role of individual differences (personality, genetic) affecting stress sensitivity. Finally, we also discuss evidence linking these opposing effects of emotion in a clinical group (PTSD), where they are both deleterious, and based on comparisons across groups with opposing affective biases: healthy aging ( positive bias ) vs. depression ( negative bias ). These issues have relevance for understanding mechanisms of emotion-cognition interactions in healthy functioning and in psychopathology, which can inspire training interventions to increase resilience and well-being.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".