The role of long-term experience in the development and use of the visual number sense

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Every time you use your smartphone, upload content to social media, or engage with Netflix or Spotify, you are interacting with a neural network: a machine-learning algorithm that has been trained to become better at recognizing and categorizing faces of you and your friends, your movie and music preferences, etc. One class of neural networks - colloquially known as "deep nets" - has been of particular interest to psychologists and neuroscientists, because the structure and behaviour of these networks in particular resembles how the brain processes visual information. Deep nets might therefore provide the missing link between machine and human intelligence, elucidating the biological mechanisms that support the brain's ability to represent, process, and predict abstract information. To what degree are deep nets representative of how human brains actually represent the world? We explore this question through the visual number sense: a perceptual system all humans have from birth that is used in everyday life when quickly estimating number, such as when guessing how many marbles are in a jar. The visual number sense not only showcases how the brain represents highly abstract information, but also correlates with other cognitive abilities, including how well we do in formal, school-taught mathematics as children and adults. Recently, researchers have designed deep nets of the visual number sense that have shown surprising alignment with human number perception (e.g., nearby numbers are harder to distinguish, the improvement in network performance is highly similar to that of children as they develop, etc.). But, to really test whether these networks are an appropriate model of human number perception, we need to derive original, still untested predictions from these networks and examine whether these same patterns occur in human observers. In this grant, we use state-of-the-art deep nets models to first derive and then test predictions about how human number perception should behave in both children and adults. For example, these networks predict that the visual number sense emerges and fine-tunes as we experience more real-world scenes - apples in a bowl, people walking on a street, etc. Therefore, the closer the experimental stimuli are to real, as opposed to artificial, scenes, the better number perception should be. By using real-world scenes (both photos and those created in 3D software), we can examine whether adults demonstrate advantages when estimating number in scenes that are more vs. less consistent with naturalistic scene statistics, and whether this advantage explains the developmental trajectory at which children's number sense improves. If, on the other hand, we fail to observe any advantages for number perception in real-world scenes, we would know that while deep nets are excellent models for machine number perception, they do not adequately capture the emergence, development, and use of visual number in human observers.