Title: Human cognition of geometric shapes, a window into the mental representation of abstract concepts
Abstract:
Natural language is not the only hallmark of humans’ singular cognitive abilities. I propose that cognition involving geometric shapes requires a set of discrete, symbolic mental representations that act as a mental language, and that perceiving a shape is performing program induction: finding the shape’s shortest representation in this internal language. First, I show that all humans share a sense of geometric complexity, but that baboons lack this sense even after training. Artificial neural networks of object recognition fit baboons’ data, but explaining humans’ behavior requires using additional symbolic properties such as the presence of right angles. Then, I identify the neural dynamics of both a visual and a symbolic strategy of shape perception using brain imaging methods, and I provide preliminary evidence for the existence of the symbolic strategy in infants. Finally, I propose and test a mental language of geometric shapes inspired by attested human geometric productions
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