Consensus Paper: Situated and Embodied Language Acquisition
Bibliographic record
Abstract
Theories of embodied cognition postulate that perceptual, sensorimotor, and affective properties of concepts support language learning and processing. In this paper, we argue that language acquisition, as well as processing, is situated in addition to being embodied. In particular, first, it is the situated nature of initial language development that affords for the developing system to become embodied. Second, the situated nature of language use changes across development and adulthood. We provide evidence from empirical studies for embodied effects of perception, action, and valence as they apply to both embodied cognition and situated cognition across developmental stages. Although the evidence is limited, we urge researchers to consider differentiating embodied cognition within situated context, in order to better understand how these separate mechanisms interact for learning to occur. This delineation also provides further clarity to the study of classroom-based applications and the role of embodied and situated cognition in the study of developmental disorders. We argue that theories of language acquisition need to address for the complex situated context of real-world learning by completing a "circular notion": observing experimental paradigms in real-world settings and taking these observations to later refine lab-based experiments.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".