Once again, the short term memory does provide some self-consistency but it is only a rudimentary form of state information. The memory is finite and covers a fixed window of a few seconds. It is not a full state model since the states here do not correspond to meaningful transitions or fundamentally different modes of operation. Thus, if no events of significance occur for a few seconds, the ARL system forgets its current state and starts off fresh. A finite state automaton will not 'forget' its current discrete state and might remain in it indefinitely until an appropriate transition is triggered. In addition, the continuous representations of the ARL's short-term memory causes some spatially driven clustering in the eigenspace. In a Hidden Markov Model (HMM), on the other hand, states are clustered in terms of their output probabilities and their generative characteristics. This is a more functional notion of state. Therefore, events that occur adjacently in time but have very different outputs will be partitioned by an HMM. However, the ARL clustering might not separate the two and cluster the events due to their spatio-temporal proximity (i.e. not their functional proximity). The notion of state can be included in the CEM algorithm if it is extended to include Hidden Markov Models in the pdf (as in addition to Gaussians).