Direct comparisons of hand and mouth kinematics during grasping, feeding and fork-feeding actions
Bibliographic record
Abstract
While a plethora of studies have examined the kinematics of human reach-to-grasp actions, few have investigated feeding, another ethologically important real-world action. Two seminal studies concluded that the kinematics of the mouth during feeding are comparable to those of the hand during grasping (Castiello, 1997; Churchill et al., 1999); however, feeding was done with a fork or spoon, not with the hand itself. Here, we directly compared grasping and feeding kinematics under equivalent conditions. Participants were presented with differently sized cubes of cheese (10-, 20- or 30-mm on each side) and asked to use the hand to grasp them or to use a fork to spear them and then bring them to the mouth to bite. We measured the apertures of the hand during grasping and the teeth during feeding, as well as reaching kinematics of the arm in both tasks. As in many past studies, we found that the hand oversized considerably larger (~11-27 mm) than the food item during grasping; moreover, the amount of oversizing scaled with food size. Surprisingly, regardless of whether the hand or fork was used to transport the food, the mouth oversized only slightly larger (~4-11 mm) than the food item during biting and the oversizing did not increase with food size. Total movement times were longer when using the fork compared to the hand, particularly when using the fork to bring food to the mouth. While reach velocity always peaked approximately halfway through the movement, relative to the reach the mouth opened more slowly than the hand, perhaps because less time was required for the smaller oversizing. Taken together, our results show that while many aspects of kinematics share some similarity between grasping and feeding, oversizing may reflect strategies unique to the hand vs. mouth (such as the need to have the digits approach the target surface perpendicularly for grip stability during lifting) and differences in the neural substrates of grasping and feeding.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".