ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2020
But Still It Moves: Static Image Statistics Underlie How We See Motion.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Human gloss perception reproduced by tiny neural networks.Nature human behaviour · 2026Article
- Hierarchical recurrent temporal prediction as a model of the mammalian dorsal visual pathway.PLoS computational biology · 2026Article
- Hierarchical motion perception as causal inference.Nature communications · 2025Article
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Optimization in Visual Motion Estimation.Annual review of vision science · 2024Review
- 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
- Neural tuning instantiates prior expectations in the human visual system.Nature communications · 2023Article
- Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation.Current biology : CB · 2022Article
- Visual motion perception as online hierarchical inference.Nature communications · 2022Article
- Prior Expectations in Visual Speed Perception Predict Encoding Characteristics of Neurons in Area MT.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- The reverse motion illusion in random dot motion displays and implications for understanding development.Journal of illusion · 2022Article
- When perception is stronger than physics: Perceptual similarities rather than laws of physics govern the perception of interacting objects.Attention, perception & psychophysics · 2022Article
- How multisensory neurons solve causal inference.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Biased orientation representations can be explained by experience with nonuniform training set statistics.Journal of vision · 2021Article
- Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model.Journal of vision · 2021Article
- Exploring and explaining properties of motion processing in biological brains using a neural network.Journal of vision · 2021Article
- A Model for Neural Network Modeling in Neuroscience.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
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Abstract
Seeing movement promotes survival. It results from an uncertain interplay between evolution and experience, making it hard to isolate the drivers of computational architectures found in brains. Here we seek insight into motion perception using a neural network (MotionNet) trained on moving images to classify velocity. The network recapitulates key properties of motion direction and speed processing in biological brains, and we use it to derive, and test, understanding of motion (mis)perception at the computational, neural, and perceptual levels. We show that diverse motion characteristics are largely explained by the statistical structure of natural images, rather than motion per se. First, we show how neural and perceptual biases for particular motion directions can result from the orientation structure of natural images. Second, we demonstrate an interrelation between speed and direction preferences in (macaque) MT neurons that can be explained by image autocorrelation. Third, we show that natural image statistics mean that speed and image contrast are related quantities. Finally, using behavioral tests (humans, both sexes), we show that it is knowledge of the speed-contrast association that accounts for motion illusions, rather than the distribution of movements in the environment (the "slow world" prior) as premised by Bayesian accounts. Together, this provides an exposition of motion speed and direction estimation, and produces concrete predictions for future neurophysiological experiments. More broadly, we demonstrate the conceptual value of marrying artificial systems with biological characterization, moving beyond "black box" reproduction of an architecture to advance understanding of complex systems, such as the brain.
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