Fast and slow processes in visuomotor adaptation: Task design and aging
Bibliographic record
Abstract
People are incredibly good at adapting their movements to altered circumstances, likely engaging multiple learning processes. How multi-process motor learning dynamics depend on the task or change with age is still unclear. In several experiments we investigate how well a two-rate model with a fast and slow process (Smith et al., 2006) can account for changes in task demands, and how age affects two-rate adaptation. Each participant separately learns two rotations in counterbalanced order and separate parts of the workspace. This way we can compare model fits on data from two tasks within each participant. We tested if two-rate model fits are affected by providing continuous or terminal feedback. Terminal feedback disambiguates error size, which would benefit error-based models of motor learning. However, adaptation with terminal feedback could be explained with a single-rate process, so that continuous feedback seems better suited for studying multi-rate motor learning. Second, we tested how older and younger adults learn either an abruptly or gradually introduced rotation. We found no effect of age, but this may be due to our choice of paradigm, in particular the final part of the task that's meant to separate the two model processes. We test this in a third experiment. Each participant does two of four different paradigms, with identical initial learning. The final parts of the task differ, affecting the model fits' fast and slow contributions to initial learning. Our results guide experimental design for two-rate model studies, and show that within-participant paradigms are both possible and useful.Acknowledgments: NSERC; DFG EG 145/1-1
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".