Does random practice impair motor skill consolidation
Bibliographic record
Abstract
Consolidation processes taking place between practice sessions are known to stabilize motor skills and improve performance without further practice (Trempe & Proteau, 2012). The recent consolidation theory also suggests that motor skills should be practiced in isolation from one another to minimize interference between the skills and optimize learning (Walker et al., 2003). This suggestion however challenges one of the most prominent theories of the 20th century suggesting that random practice (i.e., practicing two different skills alternatively during the same practice session) leads to better learning than blocked practice (i.e., practicing two different motor skills separately; Shea & Morgan, 1979). To address this discrepancy, we used a finger-sequence task in which participants had to learn in the same practice session two different sequences of finger movements. Participants either practiced the sequences in alternate order (random practice group) or separately (blocked practice group) before being retested 24 hours later. A control group also practiced only one sequence. As in previous reports, consolidation effects were measured by comparing the number of successfully completed sequences at the end of the practice session with the number of successfully completed sequences during the retention test (Walker et al., 2003). Our results revealed that the blocked practice schedule led participants to perform both sequences significantly faster during the retention test, whereas the random practice schedule led to a speed improvement of the first sequence only. Thus, random practice impaired the consolidation of the second sequence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".