Eating interrupted: the effect of intent on hand-to-mouth actions
Bibliographic record
Abstract
Evidence from recent neurophysiological studies on nonhuman primates as well as from human behavioral studies suggests that actions with similar kinematic requirements but different end-state goals are supported by separate neural networks. It is unknown whether these different networks supporting seemingly similar reach-to-grasp actions are lateralized, or if they are equally represented in both hemispheres. Recently published behavioral evidence suggests certain networks are lateralized to the left hemisphere. Specifically, when participants used their right hand, their maximum grip aperture (MGA) was smaller when grasping to eat food items than when grasping to place the same items. Left-handed movements showed no difference between tasks. The present study investigates whether the differences between grasp-to-eat and grasp-to-place actions are driven by an intent to eat, or if placing an item into the mouth (sans ingestion) is sufficient to produce asymmetries. Twelve right-handed adults were asked to reach to grasp food items to 1) eat them, 2) place them in a bib, or 3) place them between their lips and then toss them into a nearby receptacle. Participants performed each task with large and small food items, using both their dominant and nondominant hands. The current study replicated the previous finding of smaller MGAs for the eat condition during right-handed but not left-handed grasps. MGAs in the eat and spit conditions did not significantly differ from each other, suggesting that eating and bringing a food item to the mouth both utilize similar motor plans, likely originating within the same neural network. Results are discussed in relation to neurophysiology and development.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".