The Effects of Self-Guided Meditation and Napping on Memory Consolidation in Humans
Bibliographic record
Abstract
Master's thesisNumerous studies have reported that, compared to an equivalent period of wakefulness,post-training sleep (overnight or daytime naps) benefits memory consolidation (Diekelmann & Born, 2010; Mednick, Nakayama, & Stickgold, 2003; Plihal & Born, 1999; Walker et al.,2003). However, most investigations have employed various forms of “active wakefulness” (e.g., sensorimotor and cognitive tasks) as a comparison condition for sleep, while few studies have examined the role of “quiet wakefulness” in memory consolidation, even though some of the EEG oscillations during quiet waking resemble those present in sleep (e.g., increased activity in the theta-alpha range) (Brokaw et al., 2016). This study aimed to examine the consolidation of declarative (word-pair associates) andnon-declarative (marble maze visuo-motor task) learning over a 60-minutes time interval (with continuous EEG monitoring) filled with either (A) napping; (B) active-waking (watchinga video); or (C) quiet-waking (self-guided meditation).The results of the current study suggested that memory consolidation may not be a sleepspecific-phenomenon. In fact, mindfulness meditation appeared to be more advantageous than a short nap for the consolidation of declarative memories. This study also found that SWSexerts significant effects on the retention of non-declarative memory. For nappers, the absence of SWS resulted in noticeable performance enhancements compared to participants who entered SWS. Thus, it is possible that SWS plays a disadvantageous role in the consolidationof procedural memory. It is thought that sleep inertia caused by SWS is partly responsible for the impairments in tasks procedural memory. The findings of current study contribute to the understanding of memory consolidation and provide insights about the role of waking states for future studies.
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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) | 0.003 | 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".