The Language of Looking: Motion Tokens Reveal Domain-Dependent Local Structure in Gaze Sequences

Abstract:Human gaze is the primary high-bandwidth interface through which we parse the semantics of our environment. While often modeled as a sequence, the degree to which gaze exhibits transferable local structure across domains remains underexplored. Using motion tokenization as a diagnostic lens across four diverse datasets, we investigate the transferability of local sequence structure. We demonstrate that while gaze can share a reusable motion-token vocabulary, identical motion tokens are organized into different local transition patterns depending on the dataset and task context. Our results show the limitations of naive data-pooling under low-semantic motion-token representations and provide a framework for evaluating behavioral sequences through information-theoretic diagnostics.

Machine Learning (cs.LG)

arXiv:2610.16565 [cs.LG]

(or arXiv:2610.16565v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.16565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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