Attention control mediates the relationship between mental imagery vividness and emotion regulation
Bibliographic record
Abstract
Contradictory findings suggest mental imagery may both exacerbate and protect against negative affect. We aimed to reconcile these contradictory findings by considering individual differences (N=989) in imagery vividness, attention control, resilience, emotion regulation strategy, and negative affect (depressive, anxious, and posttraumatic stress symptomology). We hypothesized that attention control would mediate relationships between imagery vividness and emotion regulation strategy use, and psychopathology symptomology. Results revealed that imagery vividness, as mediated by attention control, predicted greater levels of healthy reappraisal and deleterious rumination. Attention control also mediated negative relationships between imagery vividness and catastrophizing, self-blame, and psychopathology symptomology. An exploratory latent structural equation model revealed that imagery vividness and attention control aggregated positively with reappraisal and resilience scores. The present investigation suggests an adaptive function of imagery vividness via the indirect effects of attention control, facilitating adaptive emotion regulation and limiting maladaptive strategy use, thereby protecting against negative affect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".