Hand-held tools with complex kinematics are efficiently incorporated into movement planning and online control
Bibliographic record
Abstract
Certain hand-held tools alter the mapping between hand motion and motion of the tool end point that must be controlled in order to perform a task. For example, when using a pool cue, the motion of the cue tip is reversed relative to the hand. Previous studies have shown that the time required to initiate a reaching movement (Fernandez-Ruiz J, Wong W, Armstrong IT, Flanagan JR. Behav Brain Res 219: 8-14, 2011), or correct an ongoing reaching movement (Gritsenko V, Kalaska JF. J Neurophysiol 104: 3084-3104, 2010), is prolonged when the mapping between hand motion and motion of a cursor controlled by the hand is reversed. Here we show that these time costs can be significantly reduced when the reversal is instantiated by a virtual hand-held tool. Participants grasped the near end of a virtual tool, consisting of a rod connecting two circles, and moved the end point to displayed targets. In the reversal condition, the rod translated through, and rotated about, a pivot point such that there was a left-right reversal between hand and end point motion. In the nonreversal control, the tool translated with the hand. As expected, when only the two circles were presented, movement initiation and correction times were much longer in the reversal condition. However, when full vision of the tool was provided, the reaction time cost was almost eliminated. These results indicate that tools with complex kinematics can be efficiently incorporated into sensorimotor control mechanisms used in movement planning and online control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".