Representing short-term movement to reveal animal identity and learning progress

Key points

• How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science.1
• In this paper, we ask: can short-term movement trajectories reveal learning progress and individual identity?1
• Using temporal proximity as supervision, our framework learns compact representations directly from movement trajectories without manually defining behavioral states or kinematic features.1
• 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.1

Sources (1)

• [1]Representing short-term movement to reveal animal identity and learning progress
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:35 PM

  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.

  How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science.

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