Understanding with the body? Testing the role of verb relative embodiment across tasks at the interface of language and memory
Bibliographic record
Abstract
Multiple representation accounts of conceptual knowledge argue that information associated with sensory-motor experience, in addition to pure linguistic information, contributes to word processing. A number of issues, however, remain under-investigated, including the extent to which these dimensions affect verb processing (rather than nouns), especially in languages other than English, and their role across different tasks along the language-memory continuum . Here, we collected ratings for a verb-specific dimension linked to bodily experience ( relative embodiment , RE) for 647 Italian verbs and we tested its effects in three tasks differently modulating semantic activation and memory processes (i.e., lexical decision, grammatical decision, and memory recognition). Our results showed reliable influences of RE during lexical decision and memory recognition, but not in grammatical decision, possibly due to the Italian morphological richness. The cross-task analysis showed that RE effects were substantially higher in memory recognition compared to lexical decision, indicating that semantic and episodic processes interact at the interface of language and memory. Overall, results support the flexible and context-dependent role of sensory-motor and bodily-related experience during verb processing, pointing also to language-specific factors and implications for the organization of declarative memory.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| 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".