Examining the Influence of Sensorimotor Experience on 5-year-old Children's Word Learning
Bibliographic record
Abstract
According to strong theories of embodied cognition, sensorimotor experience is essential for gaining, representing, and accessing conceptual knowledge. The role of embodied knowledge in adult language processing has been studied quite extensively, and embodied experiences are considered necessary for infants’ early learning. The effect of sensorimotor experience in older children’s language learning, however, has been examined to a much lesser extent. I conducted two experiments with 5-year-old children to examine the influence of sensorimotor interaction on object label learning. In Experiment 1, children learned labels for ten novel objects in one of four learning conditions: active interaction, observe interaction, object observation, or object observation with fact. The children were then given a recognition test, and the results indicated that there was no effect of learning condition on recognition accuracy. Children in the active interaction condition did make more comments during the learning phase about how the objects could be manipulated, and this focus on object function could have distracted from their ability to learn the object labels. In Experiment 2, I modified the stimuli so that they did not afford any obvious functions and so that the sensory features of the objects were emphasized. Children again learned labels for ten novel objects in one of two learning conditions: active interaction or object observation. Once again, there was no effect of learning condition on recognition accuracy performance. Taken together, the findings provide some insight into the role of embodied experience in children’s language learning. More specifically, the results provide evidence against strong theories of embodied cognition by demonstrating a situation in which sensorimotor experience did not benefit learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".