Integrating multi-sensory input in the body model — A RNN approach to connect visual features and motor control

An internal model of the own body can be assumed to be a central and early representation as such a model is already required in simple behavioural tasks. More and more evidence is showing that such grounded internal models are applied in higher level tasks. Internal models appear to be recruited in service for cognitive function. Understanding what another person is doing seems to rely on the ability to step into the shoes of the other person and map the observed action onto ones own action system. This rules out dedicated and highly specialized models, but presupposes a flexible internal model which can be applied in different context and fulfilling different functions. Here, we are going to present a recurrent neural network approach of an internal body model. The model can be used in the context of movement control, e.g. in reaching tasks, but can also be employed as a predictor, e.g. for planning ahead. The introduced extension allows to integrate visual features into the kinematic model. Simulation results show how in this way the model can be to utilised in perception.