Bibliographic record
Abstract
Children's earliest acquired words are often learned through sensorimotor experience, but it is less clear how children learn the meaning of concepts whose referents are less associated with sensorimotor experience. The Affective Embodiment Account postulates that children use emotional experience to learn more abstract word meanings. There is mixed evidence for this account; analyses using mega-study datasets suggest that negative or positively valenced abstract words are learned earlier than emotionally neutral abstract words, yet the relationship between sensorimotor experience and valence is inconsistent across different methods of operationalising sensorimotor experience. In the present study, we tested the Affective Embodiment Account specifically in the context of verb acquisition. We tested two semantic dimensions of sensorimotor experience: concreteness and embodiment ratings. Our analyses showed that more positive and negative abstract verbs are acquired at an earlier age than neutral abstract verbs, consistent with the Affective Embodiment Account. When sensorimotor experience is operationalised as embodiment, high embodiment verbs are acquired at an earlier age than low embodiment verbs, and there is further benefit for high embodiment and positively valenced verbs. The findings further clarify the role of Affective Embodiment as a mechanism of language acquisition.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.002 |
| 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".