Motor sequence learning involves better prediction of the next action and optimization of movement trajectories
Bibliographic record
Abstract
Abstract Learning new sequential movements is a fundamental skill for many animals. Although the behavioral manifestations of sequence learning are clear, the underlying mechanisms remain poorly understood. Motor sequence learning may arise from three distinct processes: (1) improved execution of individual movements independent of their sequential context; (2) enhanced anticipation of “what” movement should be executed next, enabling faster initiation; and (3) the development of motoric sequence-specific representations that encode “how” movements should be optimally performed within a sequence. However, many existing paradigms conflate the "what" and "how" components of learning, as participants often acquire both the sequence content (what to do) and its execution (how to do it). This overlap obscures the distinct contributions of each mechanism to motor sequence learning. In this study, we disentangled these mechanisms using a continuous reaching task. Performance in trained sequences was compared to random sequences to rule out improvements attributable solely to isolated movement execution. By also varying how many upcoming targets were visible we assessed the role of anticipation in learning. When participants could only see one future target, improvements were mostly due to them learning which target would come next. When they could see four future targets, participants immediately demonstrated fast movement times and increased movement smoothness, surpassing late-stage performance in the one target condition. Crucially, even with full visibility of future targets, participants showed further sequence-specific learning caused by a continuous optimization of movement trajectories. Follow-up experiments revealed that the learned sequence representations were effector-specific and encoded contextual information of four movements or longer. Our paradigm enables a clear dissociation between the "what" and "how" components of motor sequence learning and provides compelling evidence for the development of effector-specific sequence representations that guide optimal movement execution.
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) | 0.000 | 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) | 0.002 | 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".