Quantifying children's sensorimotor experience: Child body-object interaction ratings for 3,359 English words
Bibliographic record
Abstract
Body-object interaction (BOI) ratings measure how easily the human body can physically interact with a word's referent. Previous research has found that words higher in BOI tend to be processed more quickly and accurately in tasks such as lexical decision, semantic decision, and syntactic classification, suggesting that sensorimotor information is an important aspect of lexical knowledge. However, limited research has examined the importance of sensorimotor information from a developmental perspective. One barrier to addressing such theoretical questions has been a lack of semantic dimension ratings that take into account child sensorimotor experience. The goal of the current study was to collect Child BOI rating norms. Parents of children aged 5 – 9-years-old were asked to rate words according to how easily an average 6-year-old child can interact with each word’s referent. The relationships of Child and Adult BOI ratings with other lexical semantic dimensions were assessed, as well as the relationships of Child and Adult BOI ratings with age of acquisition. Child BOI ratings were more strongly related to valence and sensory experience ratings than Adult BOI ratings and were a better predictor of three different measures of age of acquisition. The results suggest that child-centric ratings such as those reported here provide a more sensitive measure of children’s experience that can be used to address theoretical questions in embodied cognition from a developmental perspective.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".