Targeted memory reactivation has a sleep stage‐specific delayed effect on dream content
Bibliographic record
Abstract
Although new learning is known to reappear in later dream scenarios, the timing of such reappearances remains unclear. Sometimes, references to new learning occur relatively quickly, 1 day post-learning (day-residue effect); at other times there may be a substantive delay, 5-7 days, before such references appear (dream-lag effect). We studied temporal delays in dream reactivation following the learning of a virtual reality (VR) flying task using 10-day home sleep/dream logs, and how these might be influenced by targeted memory reactivation (TMR). Participants were exposed twice to a VR task in the sleep laboratory; once before and once after a 2-hr opportunity to nap (n = 65) or to read (n = 32). Auditory cues associated with the VR task were replayed in either wake, rapid eye movement (REM) sleep, slow-wave sleep (SWS) or were not replayed. Although we previously showed that TMR cueing did not have an immediate effect on dream content, in the present study we extend these results by showing that TMR in sleep has instead a delayed effect on task-dream reactivations: participants dreamed more about the task 1-2 days later when TMR was applied in REM sleep and 5-6 days later when it was applied in SWS sleep, compared to participants with no cueing. Findings may help explain the temporal relationships between dream and memory reactivations and clarify the occurrence of day-residue and dream-lag phenomena.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".