Don't bite the hand that feeds you: A comparison of mouth and hand kinematics
Bibliographic record
Abstract
There is a long history of work investigating how vision is used to guide the arm and hand in actions such as reaching to grasp an object. Most of these studies, however, have focused on movements directed away from rather than toward the body. Yet one of the most common reasons that primates, including humans, grasp objects is to bring them to their mouths during feeding. In the present study, therefore, we examined the kinematics of arm and mouth movements in a self-directed feeding task. In particular, we were interested in whether the opening of the lips during feeding would show similar properties to the opening between the finger and thumb during grasping with a precision grip. Variously sized food items were placed at one of three distances and subjects were instructed to reach out and pick up each item, then bring it to the mouth and bite it. Two, OPTOTRAK infrared tracking systems were linked in order to record the changing positions of infrared-emitting markers placed on the arm, hand, lips and head. As is typical in kinematic studies of grasping, we found that the finger and thumb opened considerably wider than required prior to closure upon the object and that the maximum opening was reached at approximately 70% of the way through the outward reach. By comparison, the mouth opened only slightly wider than the object and did not reach its peak until the very end of the inward reach. This pattern was observed for both small and large food items. In summary, the way in which we open our hand to pick up a food object is quite different from the way in which we open our mouth during feeding. This may reflect in part the different kinds of sensory information that are used to control the two movements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".