Absorption, psychological boundaries and attitude towards dreams as correlates of dream recall: two decades of research seen through a meta‐analysis
Bibliographic record
Abstract
Many studies have reported positive correlations between dream recall frequency (DRF) and measures of absorption, psychological boundaries and attitude towards dreams. A majority of these studies, however, have relied exclusively on retrospective measures of DRF even though daily dream logs are generally considered to be more direct and valid measures of DRF. The first goal of the present meta-analysis was to evaluate the effect sizes of three variables (absorption, psychological boundaries and attitude towards dreams) as correlates of DRF. The second goal was to evaluate if these effect sizes varied as a function of how DRF was operationalized (i.e. retrospective measure versus dream log). Data from 24 studies were included in the analyses. For each of the three variables investigated, correlations with retrospective measures of DRF were of greater magnitude than those obtained with daily logs. These results indicate that scores on measures of absorption and psychological boundaries are not related to DRF per se, but rather to people's tendency to retrospectively underestimate or overestimate their DRF, while attitude towards dreams is related both to DRF per se and to people's retrospective estimation bias. Implications of these findings for dream research are discussed.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".