  In our previous study, we successfully reproduced the illusory motion of the
rotating snakes illusion using deep neural networks incorporating predictive
coding theory. In the present study, we further examined the properties of the
networks using a set of 1500 images, including ordinary static images of
paintings and photographs and images of various types of motion illusions.
Results showed that the networks clearly classified illusory images and others
and reproduced illusory motions against various types of illusions similar to
human perception. Notably, the networks occasionally detected anomalous motion
vectors, even in ordinally static images where humans were unable to perceive
any illusory motion. Additionally, illusion-like designs with repeating
patterns were generated using areas where anomalous vectors were detected, and
psychophysical experiments were conducted, in which illusory motion perception
in the generated designs was detected. The observed inaccuracy of the networks
will provide useful information for further understanding information
processing associated with human vision.
