Roles of Trait Mindfulness and Working Memory Capacity in Life Goal and Autobiographical Memory Specificities
Bibliographic record
Abstract
Low life goal and autobiographical memory specificities are associated with negative psychological symptoms. Short-term mindfulness trainings can increase life goal and autobiographical memory specificities. The present study extends the literature by investigating whether trait mindfulness is associated with life goal and autobiographical memory specificities. Additionally, because mindfulness trainings improve working memory capacity, which is associated with future episodic specificity and autobiographical memory retrieval, a second aim of this study was to examine whether working memory capacity moderates the relationship between trait mindfulness and life goal and autobiographical memory specificities. 96 participants completed the Freiburg Mindfulness Inventory, Automated Operational Span task, minimal instructions Autobiographical Memory Test, and Measure to Elicit Positive Future Goals and Plans. A multiple regression analysis revealed that the presence aspect of trait mindfulness and the interaction of the acceptance aspect of trait mindfulness and working memory capacity were predictive for goal specificity. A follow-up simple slope analysis revealed that high acceptance aspect of mindfulness was associated with low goal specificity in participants with a high working memory capacity. However, this association was not present in participants with medium and low working memory capacity. Neither trait mindfulness nor working memory capacity were associated with autobiographical memory specificity. Findings suggest that present-moment awareness enables one to focus on accessing event-specific knowledge, and that an accepting attitude alone cannot help people with high working memory capacity to make more concrete and specific future plans. The lack of association between trait mindfulness and autobiographical memory specificity might be attributed to low specific memories found in this study.
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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 | 0.003 |
| 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 | 0.000 |
| 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".