We show how event extraction can be used for handling delayed response tasks with arbitrary delay periods between the stimulus and the cue for response. We use a simple recurrent network for solving the task. Our approach is based on a number of information processing levels, where the lowest level works on raw time-step based sensory data. This data is classified using an unsupervised clustering mechanism. The second level works on this classified data, but still on the individual time-step basis. An event extraction mechanism detects and signals transitions between classes; this forms the basis for the third level. As this level only is updated when events occur, it is independent of the time-scale of the lower level interaction. We also sketch how an event filtering mechanism could be constructed which discards irrelevant data from the event stream. Such a mechanism would output a fourth level representation which could be used for delayed response tasks where irrelevant, or distracting, events could occur during the delay.
