Evidence for Automatic On-Line Adjustments of Hand Orientation During Natural Reaching Movements to Stationary Targets
Bibliographic record
Abstract
Control of the spatial orientation of the hand is an important component of reaching and grasping movements. We studied the contribution of vision and proprioception to the perception and control of hand orientation in orientation-matching and letter-posting tasks. In the orientation-matching task, subjects aligned a "match" handle to a "target" handle that was fixed in different orientations. In letter-posting task 1, subjects simultaneously reached and rotated the right hand to insert a match handle into a target slot fixed in the same orientations. Similar sensory conditions produced different error patterns in the two tasks. Furthermore, without vision of the hand, final hand-orientation errors were smaller overall in letter-posting task 1 than in the orientation-matching task. In letter-posting task 2, subjects first aligned their hand to the angle of the target and then reached to it with the instruction not to change their initial hand orientation. Nevertheless, hand orientation changed during reaching in a way that reduced the initial orientation errors. This did not occur when there was no explicitly defined target toward which the subjects reached (letter-posting task 3). The reduction in hand-orientation errors during reach, even when told not to change it, suggests the engagement of an automatic error correction mechanism for hand orientation during reaching movements toward stationary targets. The correction mechanism was engaged when the task involved transitive actions directed at the target object. The on-line adjustments can occur without vision of the hand and even when target orientation is defined only by proprioceptive inputs.
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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) | 0.001 | 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".