Bibliographic record
Abstract
Grasping objects is a fundamental skill, required to successfully interact with the environment. Most research on grasping has focused on grasping with one hand, and it has shown that grasping involves a network of fronto-parietal brain regions that controls grasps in a relatively segregated, contralateral fashion. However, one phylogenetically older form of grasping is grasping with two hands. Mechanisms underlying bimanual grasping (BMG) are not well understood, specifically how the brain's two hemispheres integrate their control processes of grasping for the two hands via the corpus callosum. BMG could either involve both hemispheres equally, requiring callosal connections at the level of motor control, or BMG could be predominately controlled by one hemisphere, only requiring callosal connections at earlier, sensory stages. To test this, we asked participants to grasp objects with both hands while fixating either to the left or right of the objects. The dependent measure was the tilt of the maximum grip aperture (MGA) in space. We predicted tilt to be forward on the side of the dominant hand. However, tilt should not be influenced by visual field if BMG were controlled by the dominant left hemisphere only. In contrast, tilt should vary across visual fields if both hemispheres coordinated their BMG control. We found the latter to be true. MGA was less tilted when participants fixated to the left side of the objects than when fixating to the right side. Our results suggest that BMG is not exclusively controlled by the left hemisphere. Further research is required to confirm whether direct input from the right visual field into the left hemisphere rather than input from the left visual field results in more coordinated bimanual grasps.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".