Embodied cognition: dynamic and information theoretic implications of embodiment

Embodiment has been discussed as being essential for understanding the mind. The goal of this presentation is to discuss implications of embodiment in a concrete way by providing case studies on the one hand, and by introducing the necessary theoretical background on the other. In particular we will demonstrate that the function of a neural network-natural or artificial-can only be understood if it is known how the neural network is embedded in the physical agent (we use the term “agent” whenever we do not want to make a distinction between humans, animals, and robots). This includes the nature of the sensors and where they are positioned on the agent. In other words, the kinds of neural signals that the agent´s neural network receives depends on its morphology. Equally important, we will demonstrate that embodiment can help us solve two of the very hard problems of cognitive science, namely (a) that agents in the real world are exposed to a continuously changing stream of sensory stimulation, and (b) the problem of object constancy (also called the scaling problem), i.e. that the sensory stimulation from one and the same object varies greatly depending on distance, viewing angle, lighting conditions, etc