Working memory training improves emotional states of healthy individuals
Bibliographic record
Abstract
Working memory (WM) capacity is associated with various emotional aspects, including states of depression and stress, reactions to emotional stimuli, and regulatory behaviors. We have previously investigated the effects of WM training (WMT) on cognitive functions and brain structures. However, the effects of WMT on emotional states and related neural mechanisms among healthy young adults remain unknown. In the present study, we investigated these effects in young adults who underwent WMT or received no intervention for 4 weeks. Before and after the intervention, subjects completed self-report questionnaires related to their emotional states and underwent scanning sessions in which brain activities related to negative emotions were measured. Compared with controls, subjects who underwent WMT showed reduced anger, fatigue, and depression. Furthermore, WMT reduced activity in the left posterior insula during tasks evoking negative emotion, which was related to anger. It also reduced activity in the left frontoparietal area. These findings show that WMT can reduce negative mood and provide new insight into the clinical applications of WMT, at least among subjects with preclinical-level conditions.
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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 | 0.000 |
| 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".