Sleep to find your way: The role of sleep in the consolidation of memory for navigation in humans
Bibliographic record
Abstract
Although a large body of evidence indicates that sleep plays an important role in learning and memory processes, the actual existence of a sleep-dependent spatial memory consolidation has been not firmly established. Here, by using a computerized 3D virtual navigation tool, we were able to show that topographical orientation in humans largely benefits from sleep after learning, while 10 h of wakefulness during the daytime do not exert similar beneficial effects. In particular, navigation performance enhancement needs sleep in the first post-training night, and no further improvements were seen after a second night of sleep. On the other hand, sleep deprivation hinders any performance enhancement and exerts a proactive disruption of spatial memory consolidation, since recovery sleep do not revert its effects. Spatial memory performance does not benefit from the simple passage of time, and a period of wakefulness between learning and sleep does not seem to have the role of stabilizing memory traces. In conclusion, our results indicate that spatial performance improvement is observed only when learning is followed by a period of sleep, regardless of the retention interval length.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".