The function of contextual illusions

Many visual illusions are contextual by nature. In the orientation-tilt illusion, the perceived orientation of a central grating is repulsed from or attracted towards the orientation of a surrounding grating. An open question in vision science is whether such illusions reflect basic limitations of the visual system, or if they correspond to corner cases of neural computations that are efficient in everyday settings. Here we develop deep recurrent network architectures that approximate neural circuits linked to contextual illusions. We show that these architectures, which we refer to as gamma-nets, are more sample efficient for learning contour detection than the state of the art, and exhibit an orientation-tilt illusion consistent with human data. Correcting this illusion significantly reduces gamma-net performance by driving it to prefer low-level edges over high-level object boundary contours. Overall, our study suggests that contextual illusions are a byproduct of neural circuits that help biological visual systems achieve robust and efficient perception, and that incorporating such circuits in artificial neural networks can improve computer vision.