From the Brain to Intelligent Systems: The Attenuation of Sensation of Self-generated Movement
Despite the recent achievements of the artificial intelligence systems, humans are still remarkably more elegant in performing a variety of sensorimotor tasks in complex and dynamically changing environments. To build machines that could learn and think like people, one needs to understand the algorithms the human brain implements to interact with the world. For an intelligent machine to independently and flexibly cope with the highly dynamical environment, discriminating self-generated changes in the environment from those generated by external agents is of critical importance. In this study, we investigated a putative mechanism of how the sensory consequences of self-generated movements are processed in the human brain. The general idea with some experimental support is that the brain actively dampens the sensory consequences of movement produced by the brain itself. To test the generality of this mechanism we conducted virtual reality (VR) experiments with human subjects where - with the help of a hand tracking device - moving targets were presented behind their own moving (but for them invisible) hand. The data from two experiments indicate attenuation of movement signals when the targets were presented behind the hand. These insights about how to cope with the sensory consequences of self-generated movement are important for building intelligent autonomous systems.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Jaan Aru, Raul Vicente