Tutorial on compositionality and self-organization in cognitive minds: Dynamic neural network models and robotics experiments (ICDL-EPIROB 2014, Genova, Oct. 13th, 2014) Jun Tani, KAIST, South Korea

The most pressing question about cognitive brains is how they support the compositionality that enables combinatorial manipulations of images, thoughts and actions. When addressing this problem with synthetic modeling, the conventional idea prevalent in artificial intelligence and cognitive science, generally, is to assume hybrid systems and corresponding neural network models, where higher-order cognition is realized by means of symbolic representation and lower sensory-motor processes by analogue processing. However, the crucial problem with such approaches is that the symbols represented at higher order cognitive levels cannot be grounded naturally in sensory-motor reality. The former are defined in a discrete space without any metric and the latter are defined in a continuous space with a physical metric. These, therefore, cannot directly interact with each other, regardless of the interface that is assigned between them.