Evidence for right-hand feeding biases in a left-handed population
Bibliographic record
Abstract
We have recently shown that actions with similar kinematic requirements, but different end-state goals may be supported by distinct neural networks. Specifically, we demonstrated that when right-handed individuals reach-to-grasp food items with intent to eat, they produce smaller maximum grip apertures (MGAs) than when they grasp the same item with intent to place it in a location near the mouth. This effect was restricted to right-handed movements; left-handed movements showed no difference between tasks. The current study investigates whether (and to which side) the effect may be lateralized in left-handed individuals. Twenty-one self-identified left-handed participants grasped food items of three different sizes while grasp kinematics were captured via an Optotrak Certus motion capture array. A main effect of task was identified wherein the grasp-to-eat action generated significantly smaller MGAs than did the grasp-to-place action. Further analysis revealed that similar to the findings in right-handed individuals, this effect was significant only during right-handed movements. Upon further inspection however, we found individual differences in the magnitude and direction of the observed lateralization. These results underscore the evolutionary significance of the grasp-to-eat movement in producing population-level right-handedness in humans as well as highlighting the heterogeneity of the left-handed population.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".