The Effects of Emotional Salience on the Day-Residue and Dream-Lag Effects
Bibliographic record
Abstract
TThere are two temporal delay effects used to describe the reoccurrence of day events in dreams. The day-residue effect is the reflection of events in dreams 1-2 nights after its occurrence and has been observed in typical and unusual day events. The dream-lag effect is the re-surfacing of daily events approximately a week after and more likely to occur when personally significant events are encountered. Further, degree of emotional intensity affects likelihood of day incorporation. The current study explores the temporal pattern of incorporation of emotionally salient day events. A sample of undergraduate psychology students (N = 45) completed a daily journal of events containing emotional importance. Nightly dream journals were also maintained for one week and were required to include as much detail as possible. Independent judges rated the number of correspondences between day events and the subsequent 7 dreams. Analysis revealed a main effect of day, main effect of emotion; negative emotions (p < 0.05) and neutral items (p < 0.01) were much more likely to be incorporated in dreams than positive emotions. In addition, there were significantly more incorporations on day 1 versus day 5 (p < 0.05) and day 7 (p < 0.05) for both negative and neutral correspondences. Overall, correspondences indicated a day-residue effect, but no dream-lag effect.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".