Dreams, Trauma, and Prediction Errors
Bibliographic record
Abstract
It is widely known that dreams can be strongly affected by traumatic events, but there may be other ways in which dreams relate to trauma. In this paper, we argue that different types of dreams could both contribute to trauma and alleviate it according to the prediction errors that occur either in dreams or in response to them after waking. A prediction error occurs when an experience contradicts one’s expectation and it is often accompanied by surprise. Prediction errors are involved in memory updating processes that can be long-lasting. Not only nightmares but also unpleasant, and surprisingly, even neutral and pleasant dreams have the potential to contribute to trauma, affecting our waking lives in a similar way to waking traumatic experiences. We postulate that certain dreams can also be beneficial for trauma alleviation. Further, clinical evidence suggests that working with prediction errors that occurred in dreams and during our response to dreams after waking can assist in alleviating the negative effects of trauma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".