Motor Force Field Learning Influences Visual Processing of Target Motion
Bibliographic record
Abstract
There are reciprocal connections between visual and motor areas of the cerebral cortex. Although recent studies have provided intriguing new insights, in comparison with volume of research on the visual control of movement, relatively little is known about how movement influences vision. The motor system is perfectly suited to learn about environmental forces. Does environmental force information, learned by the motor system, influence visual processing? Here, we show that learning to compensate for a force applied to the hand influenced how participants predicted target motion for interception. Ss trained in one of three constant force fields by making reaching movements while holding a robotic manipulandum. The robot applied forces in a null [null force field (NFF)], leftward [leftward force field (LFF)], or [rightward force field (RFF)] direction. Training was followed immediately with an interception task. The target accelerated from left to right and Ss's task was to stab it. When viewing time was optimal for prediction, the RFF group initiated their responses earlier and hit more targets, and the LFF group initiated their responses later and hit fewer targets, than the NFF group. In follow-up experiments, we show that motor learning is necessary, and we rule out the possibility that explicit force direction information drives how Ss altered their predictions of visual motion. Environmental force information, acquired by motor learning, influenced how the motion of nearby visual targets was predicted.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".