Rapid prediction of biomechanical costs during action decisions
Bibliographic record
Abstract
When given a choice between actions that yield the same reward, we tend to prefer the one that requires the least effort. Recent studies have shown that humans are remarkably accurate at evaluating the effort of potential reaching actions and can predict the subtle energetic demand caused by the nonisotropic biomechanical properties of the arm. In the present study, we investigated the time course over which such information is computed and comes to influence decisions. Two independent approaches were used. First, subjects performed a reach decision task in which the time interval for deciding between two candidate reaching actions was varied from 200 to 800 ms. Second, we measured motor-evoked potential (MEPs) to single-pulse transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) to probe the evolving decision at different times after stimulus presentation. Both studies yielded a consistent conclusion: that a prediction of the effort associated with candidate movements is computed very quickly and influences decisions within 200 ms after presentation of the candidate actions. Furthermore, whereas the MEPs measured 150 ms after stimulus presentation were well correlated with the choices that subjects ultimately made, later in the trial the MEP amplitudes were primarily related to the muscular requirements of the chosen movement. This suggests that corticospinal excitability (CSE) initially reflects a competition between candidate actions and later changes to reflect the processes of preparing to implement the winning action choice.
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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 | 0.005 |
| 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 | 0.001 | 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".