Contributions of the Left and the Right Hemispheres on Language-Induced Grip Force Modulation of the Left Hand in Unimanual Tasks
Bibliographic record
Abstract
Background and Objectives: Language-induced grip force modulation (LGFM) can be used to better understand the link between language and motor functions as an expression of embodied language. However, the contribution of each brain hemisphere to LGFM is still unclear. Using six different action verbs as stimuli, this study evaluated the grip force modulation of the left hand in a unimanual task to characterize the left and right hemispheres’ contributions. Materials and Methods: Left-hand LGFM of 20 healthy and consistently right-handed subjects was evaluated using the verbs “to write”, “to hold”, “to pull” (left-lateralized central processing actions), “to draw”, “to tie”, and “to drive” (bihemispheric central processing actions) as linguistic stimuli. The time between the word onset and the first interval of statistical significance regarding the baseline (here as reaction time, RT) was also measured. Results: The six verbs produced LGFM. The modulation intensity was similar for the six verbs, but the RT was variable. The verbs “to draw”, “to tie”, and “to drive”, whose central processing of the described action is bihemispheric, showed a longer RT compared to the other verbs. Conclusions: The possibility of a given manual action being performed by the left hand in consistent right-handers does not interfere with the occurrence of LGFM when the descriptor verb of this action is used as a linguistic stimulus, even if the possibility is remote. Therefore, LGFM seems to mainly rely on the left hemisphere, while a greater activation of the right hemisphere in action processing appears to slow the increase in LGFM intensity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".