Representations of Geometric Shapes Have Syntactic Structure

Recent research suggests that humans use language-like mental representations for many stimuli, from auditory sequences to visual shapes. However, evidence has been largely indirect, relying on stimulus compression as a proxy for internal representation. Using constituency tests, we probed representational structure in the domain of geometry more directly. Across three preregistered experiments (n= 136), we find robust evidence for tree structure in human adults’ shape representations. First, the same shape can receive different structural representations depending on how a preceding animation organizes it. Second, subparts of shapes are easier to detect when they belong to the same subtree than when spanning different subtrees. Third, shape fragments are easier to reconfigure the higher in the tree they are split. Unlike humans, state-of-the-art deep networks show no syntactic effects whatsoever. Thus, humans—and so far only humans—encode geometric shapes in hierarchical structures, mirroring the representations used in natural language processing.