Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction
By Eiji Watanabe, Akiyoshi Kitaoka, Kiwako Sakamoto, Masaki Yasugi, Kenta Tanaka. 2018
114 citations recorded by the source corpus.
The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Computer Vision and Pattern Recognition
Citations in the corpus
- Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, background): “In addition, a model trained based on the concept of predictive coding [68] was able to predict object movement and replicate motion illusions [69].”
- Evaluating large language models in theory of mind tasks (2023, background): “Models trained to process images, for example, spontaneously learned how to count ( 30 , 31 ) and differentially process central and peripheral image areas ( 32 ), as well as experience human - like optical illusions ( 33 ).”
- Theory of Mind May Have Spontaneously Emerged in Large Language Models (2023, background): “Models trained to process images, for example, spontaneously learned how to count (27, 28) and differentially process central and peripheral image areas (29), as well as experience human-like optical illusions (30).”