  Humans possess a remarkable capacity to recognize and manipulate abstract
structure, which is especially apparent in the domain of geometry. Recent
research in cognitive science suggests neural networks do not share this
capacity, concluding that human geometric abilities come from discrete symbolic
structure in human mental representations. However, progress in artificial
intelligence (AI) suggests that neural networks begin to demonstrate more
human-like reasoning after scaling up standard architectures in both model size
and amount of training data. In this study, we revisit empirical results in
cognitive science on geometric visual processing and identify three key biases
in geometric visual processing: a sensitivity towards complexity, regularity,
and the perception of parts and relations. We test tasks from the literature
that probe these biases in humans and find that large pre-trained neural
network models used in AI demonstrate more human-like abstract geometric
processing.
