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Probing Asymmetric Interactions with Time-Separated Mutual Information: A Case Study Using Golden Shiners.

Katherine Daftari1, Michael L Mayo2, Bertrand H Lemasson2

  • 1Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

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|September 27, 2024
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Summary
This summary is machine-generated.

Time-separated mutual information offers a data-efficient alternative to transfer entropy for analyzing collective animal motion. This metric accurately captures asymmetric correlations, requiring less data for reliable estimation in leader-follower dynamics.

Keywords:
animal behaviorcollective motioninformation theory

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Area of Science:

  • Collective animal behavior
  • Information theory in ecology
  • Biophysics of motion

Background:

  • Leader-follower dynamics are crucial for understanding collective animal movement.
  • Information theory metrics like transfer entropy quantify these interactions.
  • Current methods often require extensive data, limiting practical application.

Purpose of the Study:

  • To introduce time-separated mutual information as a less data-intensive metric for asymmetric correlations in collective motion.
  • To compare its efficacy against transfer entropy, especially with limited time-series data.
  • To validate the proposed metric using a generalized leader-follower model and experimental data.

Main Methods:

  • Developed a generalized leader-follower model to explore information-theoretic metrics.
  • Utilized time-separated mutual information and k-nearest neighbor algorithms for analysis.
  • Analyzed time-series trajectory data from golden shiner fish in an annular tank.

Main Results:

  • Time-separated mutual information provides a more data-efficient and accurately estimated alternative to transfer entropy.
  • A local maximum in mutual information was predicted at a specific time separation.
  • This predicted maximum corresponds to the follower's fundamental reaction timescale.
  • Experimental data confirmed the model's prediction using fish trajectory data.

Conclusions:

  • Time-separated mutual information is a robust and practical metric for quantifying asymmetric correlations in collective animal behavior.
  • The findings offer a valuable tool for researchers with limited trajectory data.
  • This approach advances the understanding of leader-follower dynamics and reaction timescales in biological systems.