Simulation Modifies Prehension: Evidence for a Conjoined Representation of the Graspable Features of an Object and the Action of Grasping It
Bibliographic record
Abstract
Movement formulas, engrams, kinesthetic images and internal models of the body in action are notions derived mostly from clinical observations of brain-damaged subjects. They also suggest that the prehensile geometry of an object is integrated in the neural circuits and includes the object's graspable characteristics as well as its semantic properties. In order to determine whether there is a conjoined representation of the graspable characteristics of an object in relation to the actual grasping, it is necessary to separate the graspable (low-level) from the semantic (high-level) properties of the object. Right-handed subjects were asked to grasp and lift a smooth 300-g cylinder with one hand, before and after judging the level of difficulty of a "grasping for pouring" action, involving a smaller cylinder and using the opposite hand. The results showed that simulated grasps with the right hand exert a direct influence on actual motor acts with the left hand. These observations add to the evidence that there is a conjoined representation of the graspable characteristics of the object and the biomechanical constraints of the arm.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".