Emotion in the mind’s eye: Imagination for adaptive cognition
Bibliographic record
Abstract
In a complex world, we are constantly faced with environmental stimuli that shape our moment-to-moment experiences. But just as rich and complex as the external world is the internal milieu—our imagination. Imagination offers a powerful vehicle for playing out hypothetical experiences in the mind’s eye. It allows us to mentally time travel to behold what the future might bring, including our greatest desires or fears. Indeed, imagined experiences tend to be emotion-laden. How and why are humans capable of this remarkable feat? Based on psychological findings, we highlight the importance of imagination for emotional aspects of cognition and behaviour, namely in the generation and regulation of emotions. Based on recent cognitive neuroscience work, we identify putative neural networks that are most critical for emotional imagination, with a large focus on the default mode network. Finally, we briefly highlight possible functional implications of individual differences in imagination. Overall, we hope to address why we as humans have the capacity to simulate hypothetical emotional experiences and how this ability can be harnessed in adaptive (and sometimes maladaptive) ways. We end by discussing open questions.
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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 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".