Representing short-term movement to reveal animal identity and learning progress
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 introduce a self-supervised representation learning framework built on a simple hypothesis: behavioral characteristics associated with identity and learning remain relatively stable over nearby time intervals. Using temporal proximity as supervision, our framework learns compact representations directly from movement trajectories without manually defining behavioral states or kinematic features. Using behavior data from freely behaving rats, we show that the learned representations not only improve prediction of identity and learning-related readouts over existing methods, but also provide a tool for investigating broader scientific questions about when and how these signals appear in behavior. Specifically, trajectories as short as one second contain informative learning and identity signals. Egocentric pose dynamics are more predictive of identity, whereas allocentric motion is more predictive of learning-related variation, and both signals are strongest at decision junctions in the alternation cohort. We further use a representation-conditioned diffusion model to inspect movement patterns associated with identity and learning progress. Our findings establish short-term movement as an informative behavioral "fingerprint" for studying learning and individual differences. Our code is available at https://github.com/pizhn/TRAIL.
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BibTeX
@misc{zhang2026representing,
title = {{Representing short-term movement to reveal animal identity and learning progress}},
author = {Ping Zhang and Quang-Huy Nguyen and Yingtie Lei and Sameera Shridhar and Zachary Leveroni and Ellie Quattrocchi and Jai Y Yu and Wei-Lun Chao},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.27140},
eprint = {2610.27140},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.27140},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Representing short-term movement to reveal animal identity and learning progress
AU - Ping Zhang
AU - Quang-Huy Nguyen
AU - Yingtie Lei
AU - Sameera Shridhar
AU - Zachary Leveroni
AU - Ellie Quattrocchi
AU - Jai Y Yu
AU - Wei-Lun Chao
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.27140
UR - https://arxiv.org/abs/2610.27140
AB - 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 introduce a self-supervised representation learning framework built on a simple hypothesis: behavioral characteristics associated with identity and learning remain relatively stable over nearby time intervals. Using temporal proximity as supervision, our framework learns compact representations directly from movement trajectories without manually defining behavioral states or kinematic features. Using behavior data from freely behaving rats, we show that the learned representations not only improve prediction of identity and learning-related readouts over existing methods, but also provide a tool for investigating broader scientific questions about when and how these signals appear in behavior. Specifically, trajectories as short as one second contain informative learning and identity signals. Egocentric pose dynamics are more predictive of identity, whereas allocentric motion is more predictive of learning-related variation, and both signals are strongest at decision junctions in the alternation cohort. We further use a representation-conditioned diffusion model to inspect movement patterns associated with identity and learning progress. Our findings establish short-term movement as an informative behavioral "fingerprint" for studying learning and individual differences. Our code is available at https://github.com/pizhn/TRAIL.
ER -