Does Napping Enhance the Consolidation of Clinically Relevant Information? A Comparison of Individuals with Low and Elevated Depressive Symptoms
Bibliographic record
Abstract
PURPOSE: Sleep, both overnight and daytime naps, can facilitate the consolidation of declarative memories in healthy humans. However, it is unclear whether such beneficial effects of sleep occur in special populations, such as individuals with elevated neuropsychiatric symptoms, and if they apply to clinically relevant material that may have personal significance to those populations. METHODS: We examined memory retention over a 60-minute interval of wakefulness or nap opportunity in participants with low or elevated scores (≤13 and ≥21, respectively) on the Beck Depression Inventory-II (BDI-II). Memory for depression-related information was assessed by (a) free-recall of a video depicting a personal experience narrative of the impact of depression on cognition and workplace performance; and (b) a paired-associates task linking depression-related cognitive symptoms to appropriate coping strategies. RESULTS: The results showed no overall difference in recall between the nap and waking condition. However, across the full sample of participants, there were significant positive correlations between total sleep time and paired associates recall, and slow wave sleep (SWS) percentage and story free recall performance. Unexpectedly, participants with elevated BDI-II scores exhibited better free-recall performance compared to those with low scores. CONCLUSION: These results suggest that sleep, specifically SWS, may stabilize memories for clinically relevant information in populations with low and elevated depressive symptoms. The superior recall in participants with elevated-BDI scores may be related to the personal significance and stronger encoding of depression-related information. These observations raise the possibility that mnemonic deficits in depressed patients may be, at least in part, related to the type of information used to assess memory performance.
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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 | 0.002 |
| 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 | 0.000 |
| 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".