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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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
