Motion shapes: Empirical studies and neural modeling

Abstract

Any mobile agent able to interact with moving objects or other mobile agents requires the ability to process motion shapes. The human visual system is an excellent, fast and proven machinery for dealing with such information. In order to obtain insight into the properties of this biological machine and to transfer it to artificial agents we analyze the limitations and capabilities of human perception of motion shapes. Here we present new empirical results on the classification, extrapolation and prediction of motion shape with varying degrees of complexity. In addition, results on the processing of multisensory spatio-temporal information will be presented. We make use of our earlier argument for the existence of a spatio-temporal memory in early vision and use the basic properties of this structure in the first layer of a neural network model. We discuss major architectural features of this network, which is based on Kohonens self-organizing maps. This network can be used as an interface to further representational stage on which motion vectors are implemented in a qualitative way. Both components of this hybrid model are constrained by the results gained in the psychophysical experiments.