Task-Specific Internal Models for Kinematic Transformations
Bibliographic record
Abstract
Numerous studies of motor learning have focused on how people adapt their reaching movements to novel dynamic and visuomotor perturbations that alter the actual or visually perceived motion of the hand. An important finding from this work is that learning of novel dynamics generalizes across different movement tasks. Thus adaptation to an unusual force field generalizes from center-out reaching movements to circular movements (Conditt et al. 1997). This suggests that subjects acquired an internal model of the dynamic environment that could be used to determine the motor commands needed for untrained movements. Using a task interference paradigm, we investigated whether transfer across tasks is also observed when learning visuomotor transformations. On day 1, all subjects adapted to a +30 degrees rotation while making center-out-and-back reaching movements. After a delay of 5 min, different groups of subjects then adapted to a -30 degrees rotation while performing either a continuous tracking task, a figure-eight drawing task, or the center-out-and-back reaching task. All subjects were then retested the next day on the +30 degrees rotation in the reaching task. As expected, subjects who experienced the opposing rotations while performing the same reaching tasks showed no retention of learning for the first rotation when tested on day 2 (Krakauer et al. 1999). In contrast, such retrograde interference was not observed in the two groups of subjects who experienced the opposing rotations while performing different tasks. In fact, their performance on day 2 was similar to that of control subjects who never experienced the opposite rotation. This lack of interference suggests that memory resources for visuomotor rotations are task specific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".