A neural network model for realizing geometric illusions based on acute-angled expansion

Recently, the study of geometric illusions has been remarkable in the field of artificial intelligence and computer vision etc., to make the spatial recognition ability of humans clearer and to apply it technologically. In the technological field, it is often supposed that the visual illusion takes place at the retina level to explain the phenomenon by the simple and unified model. However, the possibility of the illusion occurring at the higher visual field, which has been shown by various psychological and physiological studies, cannot be ignored. Recently, the acute-angled expansion recognizing the angle within the crossing lines being different from the actual angle has been reported by psychological studies. It is explained by the lateral inhibition among the orientational neurons in the cerebrum cortex. It is interesting to explain the higher visual field participating in the geometric illusion. In this study, we compose the neural network model that realizes the acute-angled expansion, by lateral inhibition among the orientational cells on the higher visual field. Moreover, we test the model by computer simulation on the intersecting line segments