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A computational model of the context processing is presented. It is shown in computer simulations how a stable context representation can be learned from a dynamic sequence of attentional shifts between various stimuli in the environment. The mechanism can automatically create the required context representations, store memories of stimuli and bind them to locations. The model also shows how an explicit matching between expected and actual stimuli can be used for novelty detection. The novelty detection system is used to decide when new binding nodes should be created and when the context representation should be shifted from one context to another. The role of context in conditioning and habituation is illustrated in two simple simulations where context learning is combined with conditioning or habituation.