Dreaming during the COVID-19 pandemic: Support for the threat simulation function of dreams
Bibliographic record
Abstract
Evolutionary theories suggest that dreams function as a world simulator of events that maximizes our ability to surmount social and threat-related challenges critical to survivorship and reproduction. Here, in contrast to the incorporation continuity hypothesis, we test the (1) social bias hypothesis, which states that dreams will overrepresent positive social interactions relative to waking life, (2) the mutually exclusive threat bias hypothesis, the idea that dream content will be negative relative to waking life, (3) the strengthening hypothesis, which states that dreams will rehearse more positive interactions with individuals the self is familiar with relative to waking life, and (4) the compensation hypothesis, which states that social contents in dreams increases during periods of social seclusion. Dream ( n = 168) and wake ( n = 184) reports were collected through a standardized online survey from 24 undergraduate students. Recalls were analyzed using the Social Content Scale. Generalized linear mixed effects models were used, and the following fixed-effects were considered for the study; the number of reports contributed, report state, biological sex, stress, social support, and media exposures. Results showed support for the threat bias hypothesis, we found that dreams were more negative and featured more unfamiliar individuals in contrast to waking life. Additionally, we found partial support for the social bias and the strengthening hypotheses, however no support was shown for the compensation hypothesis. Overall, these results demonstrate support for the threat simulation function of dreams.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".