Discrete Emotions Caused by Episodic Future Thinking: A Systematic Review With Narrative Synthesis
Bibliographic record
Abstract
Engaging in episodic future thinking, where a person imagines a specific, personal future, influences decisions partly through evoking affective experiences. While there is a growing literature on how future thinking influences affect, few studies have assessed this effect on discrete emotions. In this systematic review, we examined studies assessing the effects of episodic future thinking on discrete emotions. The aim was to provide an overview of which emotions have been studied, the evidence for an effect of future thinking on emotions, and the characteristics of emotional, episodic future thoughts. We identified 12 experimental studies (N = 2825) and synthesized these narratively. Findings suggest that episodic future thinking has some influence on several different emotions, including happiness, anxiety, and sadness. While the effects for most emotions were inconsistent, consistent effects were found for enjoyment and compassion. Imagining positive, personal future events can evoke enjoyment. Similarly, imagining instances of helping others in the future can elicit compassion. We suggest possible explanations for why future thinking only consistently influences some discrete emotions, emphasizing the cognitive appraisals and behavioral functions associated with different discrete emotions. We provide suggestions for empirically assessing effects of episodic future thinking on discrete emotions in future research.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".