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Visual motion perception as online hierarchical inference

Author

Listed:
  • Johannes Bill

    (Harvard Medical School
    Harvard University)

  • Samuel J. Gershman

    (Harvard University
    Harvard University
    MIT)

  • Jan Drugowitsch

    (Harvard Medical School
    Harvard University)

Abstract

Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.

Suggested Citation

  • Johannes Bill & Samuel J. Gershman & Jan Drugowitsch, 2022. "Visual motion perception as online hierarchical inference," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34805-5
    DOI: 10.1038/s41467-022-34805-5
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    References listed on IDEAS

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    2. David C. Bradley & Grace C. Chang & Richard A. Andersen, 1998. "Encoding of three-dimensional structure-from-motion by primate area MT neurons," Nature, Nature, vol. 392(6677), pages 714-717, April.
    3. Olivier Cappé & Eric Moulines, 2009. "On‐line expectation–maximization algorithm for latent data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 593-613, June.
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