Sensory information is not integrated following motor learning
Bibliographic record
Abstract
Previous work has demonstrated that the planning of reaches to visual (V) and proprioceptive (P) targets is mediated by distinct sensorimotor transformations (Bernier et al. 2007). In this study we asked how sensory information is integrated, and hence how these sensorimotor transformations interact, when reaching to a multimodal (visual + proprioceptive (VP)) target. Subjects trained to reach with distorted hand-cursor feedback, such that they saw a cursor that was rotated (resulting in a change of movement direction and extent) or translated (resulting in a change of movement direction) relative to their actual hand movement. Following training trials with the cursor, subjects reached to V, P and VP targets with no visual feedback of their hand. Comparison of reach endpoints revealed that reaches to VP targets followed similar trends as reaches to P targets regardless of the distortion. After reaching with a rotated cursor, subjects adapted their reaches to all target types in a similar manner. However, after reaching with a translated cursor, subjects adapted their reach to V targets only. Taken together, these results indicate that following training with a visuomotor distortion subjects rely on proprioceptive information when reaching to VP targets, implying that sensorimotor transformations do not interact. Furthermore results indicate that adaptation of reaches to V and P targets depend on the distortion presented such that training with a visuomotor rotation distortion affects the processing of both visual and proprioceptive input, while a translation distortion affects only the processing of visual input.Acknowledgments: Research support: Natural Sciences and Engineering Research Council (EKC)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".