Bibliographic record
Abstract
Recent work has shown that human subjects are able to predict the biomechanical ease and the cost of controlling the endpoint stability of potential reaching movements and to use these predictions to influence their choices between reaching movements. Here we review related experimental results in which human subjects made free choices between two potential reaching movements that varied in terms of path distance, biomechanical cost, aiming accuracy and stopping requirement, and offer a brief perspective on a potential modeling approach that would explain these results. Our main results demonstrates that prior to movement onset, control constraints such as stopping and aiming do participate in a remarkably adaptive and flexible action selection process that trades-off the advantage of moving along directions of low biomechanical cost for unconstrained movements against exploiting biomechanical anisotropies to facilitate control of endpoint stability whenever the movement constraints require it. This reveals that rather than making choices based on a minimal energy cost, the nervous system can predict additional factors, other than energy or purely abstract criteria, which may influence the decision-making process, and supports a highly context-dependent view of this process, in which the subjective desirability of potential actions may be influenced by their dynamical properties in relation to the intrinsic properties of the motor apparatus.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.516 | 0.155 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".