Evidence for an emotional adaptive function of dreams: a cross-cultural study
Bibliographic record
Abstract
The function of dreams is a longstanding scientific research question. Simulation theories of dream function, which are based on the premise that dreams represent evolutionary past selective pressures and fitness improvement through modified states of consciousness, have yet to be tested in cross-cultural populations that include small-scale forager societies. Here, we analyze dream content with cross-cultural comparisons between the BaYaka (Rep. of Congo) and Hadza (Tanzania) foraging groups and Global North populations, to test the hypothesis that dreams in forager groups serve a more effective emotion regulation function due to their strong social norms and high interpersonal support. Using a linear mixed effects model we analyzed 896 dreams from 234 individuals across these populations, recorded using dream diaries. Dream texts were processed into four psychosocial constructs using the Linguistic Inquiry and Word Count (LIWC-22) dictionary. The BaYaka displayed greater community-oriented dream content. Both the BaYaka and Hadza exhibited heightened threat dream content, while, at the same time, the Hadza demonstrated low negative emotions in their dreams. The Global North Nightmare Disorder group had increased negative emotion content, and the Canadian student sample during the COVID-19 pandemic displayed the highest anxiety dream content. In conclusion, this study supports the notion that dreams in non-clinical populations can effectively regulate emotions by linking potential threats with non-fearful contexts, reducing anxiety and negative emotions through emotional release or catharsis. Overall, this work contributes to our understanding of the evolutionary significance of this altered state of consciousness.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".