Why do we remember our dreams so well? Implications of dream recollection on the imagination vs. hallucination debate
Bibliographic record
Abstract
Why is dream memory so good? This loaded question appears to be based on an incorrect assumption, considering that bad memory of dreaming is, after all, well documented. However, whether dream memory is good or bad depends on what we compare these recollections to. Dream recollection is usually compared to memory of waking perceptual experiences, yet there is disagreement about whether this comparison is appropriate. Preliminary evidence suggests that dream memory is not bad compared to imagination memory and this may have implications for what it means to dream. Here we consider exactly how bad – or good – dream memory is compared to imagination that occurs while mindwandering and argue that while, prima facie, dream memory appears to provide an argument for the imagination model, this argument turns out to be unconvincing. There are many adverse conditions that would explain why memory of dreaming is worse than memory of waking events or hallucinations under the hallucination model. Further, if, as preliminary evidence might suggest, dream memory turns out to be somewhat better than imagination memory, this currently has no explanation under the imagination model. However, NREM dreams display the type of memory one might expect from imaginative dreaming. While the goodness or badness of memory in dreams should not be seen as a definitive argument for a particular model of dreaming, it should instead be taken as a piece of the broader landscape of abductive reasoning in this debate.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.036 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".