Untangling the causes of interference during a random practice schedule
Bibliographic record
Abstract
Motor learning involves a series of neurophysiological processes, regrouped under the term "consolidation" (McGaugh, 2000), which take place between practice sessions and are crucial for skill retention (Lohse et al., 2014; Walker, 2005). To optimize consolidation, recent results suggest that motor skills should be practiced in isolation from one another (Borragán et al., 2015; Krakauer & Shadmehr, 2006). Previous results from our laboratory supported this suggestion by showing that a random practice schedule can lead to anterograde interference (Neville & Trempe, SCAPPS 2014). Because interference is believed to occur when two tasks compete for shared resources in the brain (Ray et al., 2013), we hypothesized that this result was caused by a competition in networks either involved in movement execution and/or in acquiring the cognitive representation of the sequences. To test these two possibilities, participants learned to produce as fast and accurately as possible a 5-element sequence of fingers movements. Using a random practice schedule, participants also either typed random key presses (n = 12) or observed a novice model practice a different sequence of fingers movements (n = 11). When retention of the sequence was assessed 24 hours later, participants of both groups failed to demonstrate an increase in typing speed (p > . 26) or accuracy (p > .40) compared to their performance the day before, a result similar to what we reported when participants physically practiced two distinct sequences alternatively. Thus, our results suggest that the anterograde interference resulting from random practice has a cognitive and a motor origin.Acknowledgments: This work was funded by NSERC through an Undergraduate Student Research Award (KM Neville)
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".