Oculomotor learning is evident during implicit motor sequence learning
Bibliographic record
Abstract
Motor sequence learning involves both oculomotor and manual motor systems, yet the role of the oculomotor system in the learning and execution of skilled arm movements remains underexplored. In the current work, the influence of sequence learning on the oculomotor system was investigated by testing 20 healthy adults for 3 days as they practiced an implicit motor learning task, the serial targeting task (STT). The STT contained a repeated sequence, which was interleaved with random sequences. This task was practiced on a KINARM robot that tracked both saccades and reaches. A delayed, 24-h retention test assessed sequence-specific motor learning. Sequence-specific changes across practice and learning were observed for both saccades and reaches; this was demonstrated by faster saccade and arm motor reaction times for the repeated sequence compared to random sequences. Notably, change in the oculomotor system occurred earlier in practice as compared to the manual motor system. Reaches were executed more quickly when led by express saccades (rapid eye movements occurring within 90-120 ms) compared to when they were preceded by regular latency (> 120 ms) saccades early in practice. Our findings highlight distinct yet interconnected functions between oculomotor and manual motor systems associated with implicit motor sequence learning.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".