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For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learn- ing by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classifi- cation and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the con- creteness of words, which are in turn a predictor of children’s word learning, suggesting that these models are capturing the relationship between words and visual phenomena.
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How Children Learn From Others: An Analysis of Selective Word Learning
One way children are remarkable learners is that they learn from others. Critically, children are selective when assessing from whom to learn, particularly in the domain of word learning. We conducted an analysis of children’s selective word learning, reviewing 63 papers on 6,525 participants. Children’s ability to engage in selective word learning appeared to be present in the youngest samples surveyed. Their more metacognitive understanding that epistemic competence indicates reliability or that others are good sources of knowledge has more of a developmental trajectory. We also found that various methodological factors used to assess children influence performance. We conclude with a synthesis of theoretical accounts of how children learn from others.
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- PAR ID:
- 10289764
- Date Published:
- Journal Name:
- Child development
- Volume:
- 91
- Issue:
- 6
- ISSN:
- 1467-8624
- Page Range / eLocation ID:
- e1134-e1161
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/doi.org/10.1111/cdev.13415
