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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".