Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation

Emma Alexander*, Lanya T. Cai, Sabrina Fuchs, Tim C. Hladnik, Yue Zhang, Venkatesh Subramanian, Nicholas C. Guilbeault, Chinnian Vijayakumar, Muthukumarasamy Arunachalam, Scott A. Juntti, Tod R. Thiele, Aristides B. Arrenberg, Emily A. Cooper

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Animals benefit from knowing if and how they are moving. Across the animal kingdom, sensory information in the form of optic flow over the visual field is used to estimate self-motion. However, different species exhibit strong spatial biases in how they use optic flow. Here, we show computationally that noisy natural environments favor visual systems that extract spatially biased samples of optic flow when estimating self-motion. The performance associated with these biases, however, depends on interactions between the environment and the animal's brain and behavior. Using the larval zebrafish as a model, we recorded natural optic flow associated with swimming trajectories in the animal's habitat with an omnidirectional camera mounted on a mechanical arm. An analysis of these flow fields suggests that lateral regions of the lower visual field are most informative about swimming speed. This pattern is consistent with the recent findings that zebrafish optomotor responses are preferentially driven by optic flow in the lateral lower visual field, which we extend with behavioral results from a high-resolution spherical arena. Spatial biases in optic-flow sampling are likely pervasive because they are an effective strategy for determining self-motion in noisy natural environments.

Original languageEnglish (US)
Pages (from-to)5008-5021.e8
JournalCurrent Biology
Issue number23
StatePublished - Dec 5 2022


  • natural scene statistics
  • optic flow
  • self-motion

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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