Action verbs drive motor activity in adolescents but not in children
Bibliographic record
Abstract
In adults, grip force has reliably been used to investigate motor simulation evoked by linguistic action, suggesting that motor phenomena are linked to semantic action. The parietal and frontal lobes and their connexions are essential neural structures for pragmatic aspects of hand semantic action. In this perspective, the aim of the study was to determine the extent to which two groups of children and adolescents, classically characterized by degree of axonal myelination in fronto-parietal circuits, monitored the occurrence of nouns and manual action verbs presented auditorily while holding a grip force sensor. Differential effects of grip force were seen only in the adolescents when monitoring action verbs. Interestingly, weaker effects of grip force were modulated by noun targets only in the younger children, revealing that the ability to profit from a full semantic representation of verbs is not clearly established in the younger children. Grip force modulation was observed as early as 300 ms post target onset and peaked at the 500-750 ms window of observation for both groups. These group differences are in line with the motor simulation difficulties seen in younger children. The results may also indicate that degree of grip force in response to specific linguistic categories parallels the maturation of the parietal-frontal circuits, including the anterior intra-parietal area which plays a determining role in semantic aspects of hand action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".