Proceedings of the Annual Meeting of the Cognitive Science Society

Cognition of an object depends not only upon the sensory information of the object but also upon the context in which it occurs, as demonstrated in many psychology experiments. Although there has been considerable amount of research in cognitive science that demonstrates the importance of context, seldom has this research concerned specific computational mechanisms for learning and encoding of context. As context is largely an integration of the past up to the present, some form of information about the past stimuli must be abstracted and stored for a certain period of time so as to be used in the interpretation of the present stimulus. In this modelling approach we explore such mechanisms. In particular, we describe an unsupervised, sparsely connected, recurrent network that creates its own codings of input stimuli on ensembles of network units. Moreover, it also self-organizes itself into a short-term memory system that stores such codings. Simulations demonstrate the context-dependent recognition performance of the network.