Portable Device Validation to Study the Relation between Motor Activity and Language: Verify the Embodiment Theory through Grip Force Modulation
Bibliographic record
Abstract
Studying the link between the motor function and the linguistic function has become increasingly popular over the past decade.Often, the subject is studied with the use of expensive devices (EEG, fIRM…) limited because they need a proper space.Following the studies of Frak & al. (2010), Aravena & al. (2012-2014) and Nazir & al. (2015), at CML (Cerveau, Motricité et Langage) laboratory, we developed a portable device that analyses the grip force modulation.This device provides us with the opportunity to put in place a developmental study with children in Canada and Brazil.We analyzed the grip force modulation of fourteen Canadian teenagers (Can.) and fifteen Brazilian teenagers (Bra.) after experiencing linguistic stimulation through the use of action words (e.g.grab) and non-action word (e.g.storm).The maturity of teenagers' intraparietal area is similar to that of adults.Thus, we can compare our results with the those of Frak & al. (2010).The force modulations are analyzed using grip force sensors that are recording a variation in millinewton (mN) every millisecond (ms).Our choice in material and technic to normalize the data is based on our previous study concerning grip force sensors and linguistic stimulation.Our results show a superior modulation after listening to an action word compared to the non-action word in the two groups.We reproduce the results of Frak & al. (2010).The validation of the portable device could facilitate research by giving access to a both a larger and diverse 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".