ArticleNature communications2022
Visual motion perception as online hierarchical inference.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Prediction of uncertain visual trajectories is biased toward motion continuity.Attention, perception & psychophysics · 2026Article
- Natural scene segmentation dynamics reveal iterative Bayesian inference.bioRxiv : the preprint server for biology · 2026Article
- The neural basis for uncertainty processing in hierarchical decision making.Nature communications · 2025Article
- Environmental motion presented ahead of self-motion modulates heading direction estimation.PLoS computational biology · 2025Article
- Balancing prior knowledge and sensory data in a predictive coding model of coherent motion detection.PLoS computational biology · 2025Article
- Hierarchical motion perception as causal inference.Nature communications · 2025Article
- Hierarchical Constraints on the Distribution of Attention in Dynamic Displays.Behavioral sciences (Basel, Switzerland) · 2024Article
- Representing stimulus motion with waves in adaptive neural fields.Journal of computational neuroscience · 2024Article
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- Article
- Causal inference during closed-loop navigation: parsing of self- and object-motion.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Causal inference during closed-loop navigation: parsing of self- and object-motion.bioRxiv : the preprint server for biology · 2023Article
- Visual motion perception as online hierarchical inference.Nature communications · 2022Article
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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