Critical Brain Hypotheses on the Emergence of Cognitive Functions in Simple Circuits for Backward Time Perception

摘要

To reveal the cognitive mechanisms is one of challenging problems in various fields of science, such as neuroscience, cognitive science, and artificial intelligence. Focusing on the cognitive mechanisms of time-series events, we present an analysis on the backward time perception with the flash-lag illusion. In the flash-lag experiment, we experience the visual illusion that a stationary flashed object is perceived to lag behind a spatially aligned moving object. When this moving object changes the direction of motion, the time reversing recognition called postdiction is observed. In other words, the postdiction is a phenomenon that the future stimulus affects the present one backward in time. Although several models have been presented to explain this illusion, the neural basis is not clear. Here we propose the assumption that the simple dual-path process in the critical states can provide the backward time perception and other important features of the visual illusion related to the flash-lag experiment. The dual-path process consists of two pathways: a fast-processing pathway and a slow-processing pathway. The backward time perception can occur as a result of the integration of the information from these two pathways. We implemented the dual-path process in the critical state using a simple lattice model and succeeded in the reproduction of the postdictive property and other features of visual illusions related to the flash-lag effect. According to the criticality hypothesis proposed by Beggs, the brain operates in the critical state. If the critical state accelerates the fast pathway and decelerates the slow pathway, simple circuits can provide the complicated cognitive functions. The concept that simple systems can realize advanced functions utilizing the critical states will contribute to the discovery of the fundamental principles of neural mechanisms and the improvement of cognitive functions of artificial intelligence.