Restricted transfer of learning between unimanual and bimanual finger sequences
Bibliographic record
Abstract
When training bimanual skills, such as playing piano, people sometimes practice each hand separately and at a later stage combine the movements of the two hands. This poses the critical question of whether motor skills can be acquired by separately practicing each subcomponent or should be trained as a whole. In the present study, we addressed this question by training human subjects for 4 days in a unimanual or bimanual version of the discrete sequence production task. Both groups were then tested on trained and untrained sequences on both unimanual and bimanual versions of the task. Surprisingly, we found no evidence of transfer from trained unimanual to bimanual or from trained bimanual to unimanual sequences. In half the participants, we also investigated whether cuing the sequences on the left and right hand with unique letters would change transfer. With these cues, untrained sequences that shared some components with the trained sequences were performed more quickly than sequences that did not. However, the amount of this transfer was limited to ∼10% of the overall sequence-specific learning gains. These results suggest that unimanual and bimanual sequences are learned in separate representations. Making participants aware of the interrelationship between sequences can induce some transferrable component, although the main component of the skill remains unique to unimanual or bimanual execution. NEW & NOTEWORTHY Studies in reaching movement demonstrated that approximately half of motor learning can transfer across unimanual and bimanual contexts, suggesting that neural representations for unimanual and bimanual movements are fairly overlapping at the level of elementary movement. In this study, we show that little or no transfer occurred across unimanual and bimanual sequential finger movements. This result suggests that bimanual sequences are represented at a level of the motor hierarchy that integrates movements of both hands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".