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How do children’s representations of object categories change as they grow older? As they learn about the world around them, they also express what they know in the drawings they make. Here, we examine drawings as a window into how children represent familiar object categories, and how this changes across childhood. We asked children (age 3-10 years) to draw familiar object categories on an iPad. First, we analyzed their semantic content, finding large and consistent gains in how well children could produce drawings that are recognizable to adults. Second, we quantified their perceptual similarity to adult drawings using a pre-trained deep convolutional neural network, allowing us to visualize the representational layout of object categories across age groups using a common feature basis. We found that the organization of object categories in older children’s drawings were more similar to that of adults than younger children’s drawings. This correspondence was strong in the final layers of the neural network, showing that older children’s drawings tend to capture the perceptual features critical for adult recognition. We hypothesize that this improvement reflects increasing convergence between children’s representations of object categories and that of adults; future work will examine how these age-related changes relate to children’s developing perceptual and motor capacities. Broadly, these findings point to drawing as a rich source of insight into how children represent object concepts.
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Developmental changes in the ability to draw distinctive features of object categories
How do children’s visual concepts change across childhood, and how might these changes be reflected in their drawings? Here we investigate developmental changes in children’s ability to emphasize the relevant visual distinctions between object categories in their drawings. We collected over 13K drawings from children aged 2-10 years via a free-standing drawing station in a children’s museum. We hypothesized that older children would produce more recognizable drawings, and that this gain in recognizability would not be entirely explained by concurrent development in visuomotor control. To measure recognizability, we applied a pretrained deep convolutional neural network model to extract a high-level feature representation of all drawings, and then trained a multi-way linear classifier on these features. To measure visuomotor control, we developed an automated procedure to measure their ability to accurately trace complex shapes. We found consistent gains in the recognizability of drawings across ages that were not fully explained by children’s ability to accurately trace complex shapes. Furthermore, these gains were accompanied by an increase in how distinct different object categories were in feature space. Overall, these results demonstrate that children’s drawings include more distinctive visual features as they grow older.
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- Award ID(s):
- 1714726
- PAR ID:
- 10128364
- Date Published:
- Journal Name:
- Proceedings of the 41st Annual Conference of the Cognitive Science Society.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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