Are You Gonna Eat That? (Your brain says "yes," but your body says "maybe.")
Bibliographic record
Abstract
Evidence from recent neurophysiological studies on non-human primates as well as from human behavioural studies suggest that actions with similar kinematic requirements (i.e., reach-to-grasp) but different end-state goals (e.g., grasp-to-place versus grasp-to-throw) are supported by different neural networks. However, it is unknown whether these different networks supporting seemingly similar reach-to-grasp actions are lateralized, or if they are present in both hemispheres. Recently, we provided behavioural evidence suggesting they are lateralized to the left hemisphere. Specifically, we observed that when participants used their right hand their maximum grip aperture (MGA) was smaller when grasping-to-eat food items compared to when grasping-to-place the same items. Left-handed movements show no difference between tasks. Given that grasp-to-eat actions are fundamental for human survival, we interpreted this finding as a potential driver of population-level right-handedness. In the present study we investigate whether the differences between grasp-to-eat and grasp-to-place actions are driven by an intent to eat the food, or if placing it into the mouth (sans ingestion) is sufficient to produce asymmetries. Twelve right-handed adults were asked to reach-to-grasp food items to either a) eat the item, b) place it in a bib below his/her chin, or c) briefly place the item between his/her lips, then spit it into a nearby bin. Participants performed each task with large/small food items, using their dominant and non-dominant hands (hand/task order counterbalanced). MGAs (measured by an Optotrak camera system) were analyzed using a 2 (Hand; right/left) x2 (Size; small/large) x3 (Task; eat/place/spit) ANOVA. Our results replicated our previous finding of smaller MGAs for the eat condition during right-handed grasps only. Furthermore, 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. Meeting abstract presented at VSS 2014
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".