Motivated Prediction of Future Feelings: Effects of Negative Mood and Mood Orientation on Affective Forecasts
Bibliographic record
Abstract
This article examines the role of motivational factors in affective forecasting. The primary hypothesis was that people predict positive emotional reactions to future events when they are motivated to enhance their current feelings. Three experiments manipulated participants' moods (negative vs. neutral) and orientation toward their moods (reflective vs. ruminative) and then assessed the positivity of their affective predictions for future events. As hypothesized, when participants adopted a reflective orientation, and thus should have been motivated to engage in mood-regulation processes, they predicted more positive feelings in the negative than in the neutral mood condition. This pattern of mood-incongruent affective prediction was not exhibited when participants adopted a ruminative orientation. Additionally, within the negative mood condition, generating affective forecasts had a more positive emotional impact on reflectors than on ruminators. The findings suggest that affective predictions are sometimes driven by mood-regulatory motives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".