Sleep physiology predicts memory retention after reactivation
Bibliographic record
Abstract
Both sleep and future relevance influence memory consolidation; however, limited research has investigated their role in memory reconsolidation. We manipulated the future relevance of both stable and labile memories in need of reconsolidation. Two groups learned two blocks of syllable pairs on one evening and were told they would be tested on one of the blocks later. On the second evening, one group (Labile) received reminders designed to return their memories of syllable pairs to a labile state, while a second group (Stable) received reminders designed to leave these memories in a stable state. No significant differences in memory retention were found between blocks or groups the following morning. Frontal delta (0.5-4 Hz) electroencephalographic power during Stage 2 sleep correlated positively with retention of future-relevant material exclusively in the Labile group. Central theta (4-8 Hz) electroencephalographic power during rapid eye movement (REM) sleep correlated positively with the extent to which the Labile group selectively retained future-relevant material. These relationships suggest that sleep-dependent processes are involved in an economical reprocessing of memories beyond the initial stages of consolidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".