Bibliographic record
Abstract
In this chapter we look at the role that sensory motor activation plays in the understanding of figurative and bilingual language. The chapter is divided into three basic parts. First we examine what is known about the evolution of human language, with reference to figurative and bilingual language activities, emphasizing the emerging conceptualization that sensory-motor brain areas have played a vital role. In the next section we examine how this emerging conceptualization that language might be embodied has been translated into our understanding of online comprehension tasks in general and, increasingly, in grounding our understanding of figurative language. The last section examines how the notion of embodied cognition has been viewed in our understanding of bilingual language, noting the near absence of a relevant literature. We conclude by indicating some aspects of the archival bilingual processing literature that could benefit from taking an embodied perspective. Keywords: bilingual embodiment, embodied cognition, figurative language, language evolution, metaphor processing The classic approach in both the study of bilingualism and of figurative language has taken an amodal computational perspective. From this perspective, these models have been based on the assumption that the basic representational aspects of language are tied to symbols, which themselves are not tied to direct experience with the environments in which they have developed and in which they are expressed. In contrast, starting about a decade or so ago, an alternative approach has emerged in which language comprehension is directly and inextricably tied to a relationship between bodily experiences and language.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".