arXiv — cs.AI preprintsInternational21 October 2026
Representing short-term movement to reveal animal identity and learning progress
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arXiv:2610.27140v1 Announce Type: cross Abstract: How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science. However, a major challenge is identifying observable behavioral measures that faithfully capture learning progression. Existing approaches commonly rely on predefined behavioral features or task-level performance, while fine-scale posture and movement in freely behaving animals remain less explored. In this paper, we ask: can short-term movement trajectories reveal learning progress and individual identity? We introduc
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