Gravity-dependent estimates of object mass underlie the generation of motor commands for horizontal limb movements
Bibliographic record
Abstract
Moving requires handling gravitational and inertial constraints pulling on our body and on the objects that we manipulate. Although previous work emphasized that the brain uses internal models of each type of mechanical load, little is known about their interaction during motor planning and execution. In this report, we examine visually guided reaching movements in the horizontal plane performed by naive participants exposed to changes in gravity during parabolic flight. This approach allowed us to isolate the effect of gravity because the environmental dynamics along the horizontal axis remained unchanged. We show that gravity has a direct effect on movement kinematics, with faster movements observed after transitions from normal gravity to hypergravity (1.8g), followed by significant movement slowing after the transition from hypergravity to zero gravity. We recorded finger forces applied on an object held in precision grip and found that the coupling between grip force and inertial loads displayed a similar effect, with an increase in grip force modulation gain under hypergravity followed by a reduction of modulation gain after entering the zero-gravity environment. We present a computational model to illustrate that these effects are compatible with the hypothesis that participants partially attribute changes in weight to changes in mass and scale incorrectly their motor commands with changes in gravity. These results highlight a rather direct internal mapping between the force generated during stationary holding against gravity and the estimation of inertial loads that limb and hand motor commands must overcome.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".