The role of phasic and tonic rapid eye movement sleep in implicit memory consolidation
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep is essential for memory consolidation, yet its role in implicit visuomotor memory remains poorly understood. Rapid eye movement (REM) sleep, particularly its Phasic and Tonic sub-states, may contribute to this process. This study revisits data from Viczko et al. (2018) to examine Phasic and Tonic REM characteristics and electroencephalography activity in implicit visuomotor memory consolidation using the Serial Reaction Time Task. METHODS: Participants trained on an implicit visuomotor sequence and were tested post-training, after a sleep/wake interval, and 1 week later. Polysomnography was used to analyze sleep architecture, focusing on REM sub-states. Electroencephalography data during REM were assessed for theta power (~4-8 Hz) using event-related spectral perturbation. Eye movements were categorized into Phasic or Tonic REM, with task-specific regional activity examined over frontal (Fz) and motor areas (C4). RESULTS: Increases in total REM sleep and Tonic REM sleep duration were observed during the experimental compared to the control night. Changes in Tonic REM sleep correlated with motor representation speed post-sleep. Event-related spectral perturbation analysis revealed enhanced theta power time-locked to Phasic and Tonic REM eye movements, with regionally specific task-related increases over Fz and C4 (but not contralaterally). CONCLUSIONS: Findings suggest Tonic REM stabilizes visuomotor memory, with theta oscillations serving as electrophysiological markers of consolidation. Task-specific theta increases highlight regional specificity of memory processing during sleep. This study underscores the roles of REM sub-states in implicit visuomotor memory consolidation and the need to further explore sleep sub-states.
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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 | 0.001 |
| 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) | 0.001 | 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".