Emotional mental imagery generation during spontaneous future thinking: relationship with optimism and negative mood
Bibliographic record
Abstract
Optimism is known to buffer against negative mood. Thus, understanding the factors that contribute to individual variation in optimism may inform interventions for mood disorders. Preliminary evidence suggests that the generation of mental imagery-based representations of positive relative to negative future scenarios is related to optimism. This study investigated the hypothesis that an elevated tendency to generate positive relative to negative mental imagery during spontaneous future thinking would be associated with reduced negative mood via its relationship to higher optimism. Participants (N = 44) with varied levels of naturally occurring negative mood reported current levels of optimism and the real-time occurrence and characteristics of spontaneous thoughts during a sustained attention computer task. Consistent with hypotheses, higher optimism statistically mediated the relationship between a higher proportional frequency of positive relative to negative mental imagery during spontaneous future thinking and lower negative mood. Further, the relationship between emotional mental imagery and optimism was found for future, but not past, thinking, nor for verbal future or past thinking. Thus, a greater tendency to generate positive rather than negative imagery-based mental representations when spontaneously thinking about the future may influence how optimistic one feels, which in turn may influence one’s experience of negative mood.
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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 | 0.007 |
| 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 | 0.000 |
| 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".