Consolidation through the looking‐glass: sleep‐dependent proactive interference on visuomotor adaptation in children
Bibliographic record
Abstract
Although a beneficial role of post-training sleep for declarative memory has been consistently evidenced in children, as in adults, available data suggest that procedural memory consolidation does not benefit from sleep in children. However, besides the absence of performance gains in children, sleep-dependent plasticity processes involved in procedural memory consolidation might be expressed through differential interference effects on the learning of novel but related procedural material. To test this hypothesis, 32 10-12-year-old children were trained on a motor rotation adaptation task. After either a sleep or a wake period, they were first retested on the same rotation applied at learning, thus assessing offline sleep-dependent changes in performance, then on the opposite (unlearned) rotation to assess sleep-dependent modulations in proactive interference coming from the consolidated visuomotor memory trace. Results show that children gradually improve performance over the learning session, showing effective adaptation to the imposed rotation. In line with previous findings, no sleep-dependent changes in performance were observed for the learned rotation. However, presentation of the opposite, unlearned deviation elicited significantly higher interference effects after post-training sleep than wakefulness in children. Considering that a definite feature of procedural motor memory and skill acquisition is the implementation of highly automatized motor behaviour, thus lacking flexibility, our results suggest a better integration and/or automation or motor adaptation skills after post-training sleep, eventually resulting in higher proactive interference effects on untrained material.
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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 | 0.001 |
| 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".