Targeted memory reactivation elicits temporally compressed reactivation linked to spindles
Bibliographic record
Abstract
Abstract Memories reactivate during sleep, however the properties of such reactivation and its relationship to encoding strength and subsequent memory performance are not well understood. We set out to examine memory reactivations associated with a serial reaction time task (SRTT). 48 human participants performed the SRTT, and then slept in the lab while we deliberately induced reactivation in Slow Wave Sleep (SWS) using a Targeted Memory Reactivation (TMR) design. We detected reactivation after TMR cues using multiclass classification that adapted to sleep data by using sleep activity for training and wake activity for testing. We then examined the temporal properties of reactivation in relation to behavioural performance and sleep spindles. In keeping with the rodent literature, the observed reactivation was 3 to 20 times faster than waking activity. Furthermore, we report an inverted-U shaped relationship between TMR-related behavioural improvement and encoding strength, with very strong and very weak memories benefiting little from cueing while medium-strength memories benefit the most. Finally, reactivation was more frequently observed in trials with high sigma power, supporting the idea that sleep spindles are associated with memory reactivation during sleep. These findings bring us closer to understanding the characteristics of human memory reactivation after TMR, demonstrate when cueing is effective, and provide evidence for the positive relationship between the detectability of reactivation and memory consolidation.
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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".