Embodied Comprehension of Stories: Interactions between Language Regions and Modality-specific Neural Systems
Bibliographic record
Abstract
The embodied view of language processing proposes that comprehension involves multimodal simulations, a process that retrieves a comprehender's perceptual, motor, and affective knowledge through reactivation of the neural systems responsible for perception, action, and emotion. Although evidence in support of this idea is growing, the contemporary neuroanatomical model of language suggests that comprehension largely emerges as a result of interactions between frontotemporal language areas in the left hemisphere. If modality-specific neural systems are involved in comprehension, they are not likely to operate in isolation but should interact with the brain regions critical to language processing. However, little is known about the ways in which language and modality-specific neural systems interact. To investigate this issue, we conducted a functional MRI study in which participants listened to stories that contained visually vivid, action-based, and emotionally charged content. Activity of neural systems associated with visual-spatial, motor, and affective processing were selectively modulated by the relevant story content. Importantly, when functional connectivity patterns associated with the left inferior frontal gyrus (LIFG), the left posterior middle temporal gyrus (pMTG), and the bilateral anterior temporal lobes (aTL) were compared, both LIFG and pMTG, but not the aTL, showed enhanced connectivity with the three modality-specific systems relevant to the story content. Taken together, our results suggest that language regions are engaged in perceptual, motor, and affective simulations of the described situation, which manifest through their interactions with modality-specific systems. On the basis of our results and past research, we propose that the LIFG and pMTG play unique roles in multimodal simulations during story comprehension.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".