Learning versus learned: Flexible control processes when reaching with altered visual feedback of the hand's position
Bibliographic record
Abstract
We examined control processes underlying reaches when participants were first introduced to a visuomotor distortion (Experiment 1) and then following visuomotor adaptation (Experiment 2). All participants completed 150 reach training trials when (1) a cursor accurately represented their hand motion (i.e., aligned cursor) and (2) a cursor was rotated 45 degrees clockwise relative to their hand motion (i.e., rotated cursor). In Experiment 1, participants (n = 16) received either continuous visual feedback or terminal visual feedback regarding movement endpoint during reach training. Analyses revealed that participants were able to demonstrate similar outcomes (i.e., movement time and endpoint errors) regardless of visual or cursor feedback, but demonstrated more offline control (i.e., took more time and were less consistent in initiating their movements) when reaching with a rotated cursor compared to an aligned cursor, even at the end of training. In attempt to promote online corrective processes, participants (n = 16) in Experiment 2 first completed the training trials with continuous visual feedback and then completed an additional 45 reaches under (1) slow MT (800-1000 ms) and (2) fast MT constraints (400-500 ms). Results showed a shift to online control (i.e., greater endpoint accuracy) when reaching with an aligned and rotated cursor, when sufficient time was provided (i.e., Slow MT). Together, these experiments demonstrate a flexibility in control processes underlying reaches with rotated visual feedback of the hand, in that reaches first engage offline control processes during learning, and then shift to online corrective processes once the visual distortion is learned.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC); awarded to the last author.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".