Research in embodied cognitive science emphasizes that a close interaction of brain, body and environment is central to the emergence of cognitive processes. Much work on embodied artificial intelligence has therefore shifted focus from purely computational modeling to autonomous mobile robotics. Many researchers emphasize the importance of working with real robots rather than simulations which usually cannot fully capture the complexities of the physical world. However, from a cognitive science point of view, robot simulations nevertheless have an important, complementary role to play, due to the fact that in many cases they allow for more extensive, systematic experimentation as well as for experiments, e.g. with evolving robot morphologies, that can only be carried out in very limited form on real robots. Furthermore, it will be argued in this paper, robot simulations are very useful tools in experimentation with active adaptation of non-trivial environments, an aspect that is still largely ignored in much embodied artificial intelligence research. 1

A longitudinal study of immediate, deferred, and synchronic imitation through the second

Fodor and Pylyshyn (1988) argue that connectionist models are not able to display systematicity other than by implementing a classical symbol system. This claim entails that connectionism cannot compete with the classical approach as an alternative architectural framework for human cognition. We present a connectionist model of sentence comprehension that does not implement a symbol system yet behaves systematically. It consists in a recurrent neural network that maps sentences describing situations in a microworld, onto representations of these situations. After being trained on particular sentences-situation pairs, the model can comprehend new sentences, even if these describe new situations. We argue that this systematicity arises robustly and in a psychologically plausible manner because it depends on structure inherent in the world.

The authors would like to thank Prof. Paul Smolensky for his comments and suggestions. This work