Spatial Semantic Pointers (SSPs) have recently emerged as a powerful tool for representing and transforming continuous space, with numerous applications to cognitive modelling and deep learning. Fundamental to SSPs is the notion of similarity between vectors representing different points in $n$-dimensional space -- typically the dot product or cosine similarity between vectors with rotated unit-length complex coefficients in the Fourier domain. The similarity measure has previously been conjectured to be a Gaussian function of Euclidean distance. Contrary to this conjecture, we derive a simple trigonometric formula relating spatial displacement to similarity, and prove that, in the case where the Fourier coefficients are uniform i.i.d., the expected similarity is a product of normalized sinc functions: $prod_{k=1}^{n} operatorname{sinc} left( a_k right)$, where $mathbf{a} in mathbb{R}^n$ is the spatial displacement between the two $n$-dimensional points. This establishes a direct link between space and the similarity of SSPs, which in turn helps bolster a useful mathematical framework for architecting neural networks that manipulate spatial structures.
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