A neuroanatomical examination of embodied cognition: semantic generation to action-related stimuli
Bibliographic record
Abstract
The theory of embodied cognition postulates that the brain represents semantic knowledge as a function of the interaction between the body and the environment. The goal of our research was to provide a neuroanatomical examination of embodied cognition using action-related pictures and words. We used functional magnetic resonance imaging (fMRI) to examine whether there were shared and/or unique regions of activation between an ecologically valid semantic generation task and a motor task in the parietal-frontocentral network (PFN), as a function of stimulus format (pictures versus words) for two stimulus types (hand and foot). Unlike other methods for neuroimaging analyses involving subtractive logic or conjoint analyses, this method first isolates shared and unique regions of activation within-participants before generating an averaged map. The results demonstrated shared activation between the semantic generation and motor tasks, which was organized somatotopically in the PFN, as well as unique activation for the semantic generation tasks in proximity to the hand or foot motor cortex. We also found unique and shared regions of activation in the PFN as a function of stimulus format (pictures versus words). These results further elucidate embodied cognition in that they show that brain regions activated during actual motor movements were also activated when an individual verbally generates action-related semantic information. Disembodied cognition theories and limitations are also discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".