Development of Abstract Word Knowledge
Bibliographic record
Abstract
The development of children's word knowledge is an important testing ground for the embodied account of word meaning, which proposes that word meanings are grounded in sensorimotor systems. Acquisition of abstract words, in particular, is a noted challenge for strong accounts of embodiment. We examined acquisition of abstract word meanings, using data on development of vocabulary knowledge from early school to University ages. We tested two specific proposals for how abstract words are learned: the affective embodiment account, that emotional experience is key to learning abstract word meanings, and the learning through language proposal, that abstract words are acquired through language experience. We found support for the affective embodiment account: word valence, interoception, and mouth action all facilitated abstract word acquisition more than concrete word acquisition. We tested the learning through language proposal by investigating whether words that appear in more diverse linguistic contexts are earlier acquired. Results showed that contextual diversity facilitated vocabulary acquisition, but did so for both abstract and concrete words. Our results provide evidence that emotion and sensorimotor systems are important to children's acquisition of abstract words, but there is still considerable variance to be accounted for by other factors. We offer suggestions for future research to examine the acquisition of abstract vocabulary.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".