Online and offline contributions to recently acquired reaching movements
Bibliographic record
Abstract
The use of sensory feedback for online and offline movement corrections has been widely studied for well learned actions, but not for actions in a novel visuomotor environment. We asked if the contributions of online and offline motor control differ between recently acquired reaching movements from well learned reaching. Eight participants were divided into 2 groups, one receiving continuous visual feedback during all reaches (CF), and another receiving terminal feedback regarding movement endpoint (TF). Participants then trained in a visuomotor environment by reaching to 3 targets when (1) a cursor accurately represented their hand motion and (2) a cursor was rotated 45 degrees clockwise relative to their hand motion. After training in each visuomotor environment, contributions were then probed by having participants complete 4 blocks of reaches with constrained reaction time (RT) and movement time (MT) (SlowRT-SlowMT, SlowRT-FastMT, FastRT-SlowMT and FastRT-FastMT). Participants demonstrated similar performance (i.e. MT and angular errors) regardless of feedback or reaching environment during training. Once constraints were imposed, offline control measures (i.e., squared Fisher z) indicated that early movement positions were more predictive of movement end point when reaching with a rotated cursor compared to an aligned cursor for both groups. Alternatively, online control measures (i.e., time to peak velocity and jerk score) did not differ for reaches completed with aligned or rotated feedback for either group. Together, these results suggest a greater contribution of offline control processes when reaching in a novel visuomotor environment compared to when reaching in a well learned environment.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC); awarded to the last author.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".