Timing and spatial accuracy of reaching movements do not improve off-line
Bibliographic record
Abstract
Consolidation, a time-dependant process allowing the newly acquired motor skill to be stored in long-term memory, is essential to motor learning. In sequence production tasks, consolidation has even been associated with performance gains without additional practice (i.e., off-line learning). However, the movement characteristics improved off-line and causing the performance gains remain poorly understood. To investigate this question, thirty-eight subjects (15 males, 23 females; mean age: 23.9 ± 3.4) were trained to produce a sequence of planar reaching movements toward four different visual targets. The task required that participants learn the relative timing of the movements of the sequence (i.e., the duration of each movement in proportion to the other movements), the absolute timing (i.e., the speed at which the whole sequence should be executed) and aim accurately at each target. Participants performed a first training session (150 trials) during which they received visual and temporal feedback following each trial. Off-line learning was assessed by comparing the performance of two groups performing a no-feedback retention test either 10-min or 24-hour after the initial practice session. Our results indicated that a 24-hour consolidation interval did not result in better temporal or spatial precision (p > 0.11, np2 > 0.07) nor a decrease in the participants' variability (p > 0.39, np2 < 0.02). This absence of off-line gains, also observed in other paradigms using gross motor tasks, suggests that off-line learning may be restricted to sequence production tasks in which the different sub-movements must be regrouped (chunked) together to accelerate their execution.Acknowledgments: Luc Proteau, Marcel Beaulieu
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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 | 0.001 |
| 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".