Neurodynamics of intentional behavior generation

Abstract

The chapter reviews mechanisms of generation and utilization of knowledge in human cognitive activity and in artificial intelligence systems. First we explore experience-based methods, including top-down symbolic approaches, which address knowledge processing in humans. Symbolic theories of intelligence fall short of explaining and implementing strategies routinely produced by human intelligence. Connectionist methods are rooted in our understanding of the operation of brains and nervous systems, and they gain popularity in constructing intelligent devices. Contrary to top-down symbolic methods, connectionism uses bottom-up emergence to generate intelligent behaviors. Recently, computational intelligence, cognitive science and neuroscience have achieved a level of maturity that allows integration of top-down and bottom-up approaches, in modeling the brain. We presenta dynamical approach to higher cognition and intelligence based on the model of intentional action-perception cycle. In this model, meaningful knowledge is continuously created, processed, and dissipated in the form of sequences of oscillatory patterns of neural activity distributed across space and time, rather than via manipulation of certain symbol system. Oscillatory patterns can be viewed as intermittent representations of generalized symbol systems, with which brains compute. These dynamical symbols are not rigid but flexible and they disappear soon after they have been generated through spatio-temporal phase transitions, at the rate of 4-5 patterns per second in human brains. Human cognition performs a granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehendible chunks of concepts and complex behavioral schemas. They are accessed during future action selection and decisions. This biologically-motivated computing using dynamic patterns provides an alternative to the notoriously difficult symbol grounding problem and it has been implemented in computational and robotic environments. © 2007 Springer-Verlag Berlin Heidelberg.