Co-imagination fosters shared emotions of future experiences
Bibliographic record
Abstract
Emotions play a crucial role in a host of goal-directed cognitive processes, such as imagining and planning for the future. From hope to despair, shared emotions within representations of the future can motivate farsighted decisions and facilitate social coordination. Though interpersonal dynamics are critical to theories of emotion, imagination has been primarily studied as a process occurring within individuals rather than between them. Nevertheless, humans readily imagine their futures together. Here, we test the hypothesis that such collaborative imagination (co-imagination) of shared future experiences promotes emotional convergence in future event representations among individuals. In two experiments involving university and Prolific participants (N=204), we use natural language models to code individual narratives for a rich and complex array of emotional states. These studies demonstrate that co-imagination in novel dyads fosters alignment in the emotions partners express within their individual representations of the shared future, more so than independently imagining future events using the same cues. This work illuminates a new framework and mechanism for the formation of shared emotions within representations of the future.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".