Mapping somatosensory vs. visual targets for the online control of the unseen limb
Bibliographic record
Abstract
When performing goal-directed upper-limb reaches to a visual target, the sensorimotor system can adjust the ongoing movement to changes in target location, even without vision of the reaching limb. These limb trajectory amendments may be based on visual information about the new target location and online somatosensory inputs from the unseen hand processed in a visual reference frame. The purpose of the present study was to determine if the sensorimotor transformations used for the online control of unseen hand movements to somatosensory targets occurs in a visual or a non-visual reference frame. Reaches were made towards a somatosensory or a visual targets that either remained stable, or changed position: before movement onset (~450 ms), or 100 ms or 200 ms after. In response to both the 100 ms and 200 ms perturbations, participants exhibited shorter correction latencies, larger correction amplitudes, and smaller endpoint errors, when reaching to somatosensory targets compared to when reaching to visual targets. These results indicate that, for the online control of somatosensory target perturbations, hand position was unlikely transformed into visual reference frame prior to the initiation of corrections.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF), University of Toronto
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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) | 0.000 | 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) | 0.002 | 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".