According to Noë´s enactive theory of perception, sensorimotor knowledge allows us to predict the sensory outcomes of our actions. This paper suggests that tuning input filters with such predictions may be the cause of sustained inattentional blindness. Most models of learning capture statistically salient regularities in and between data streams. Such analysis is, however, severely limited by both the problem of marginal regularity and the credit assignment problem. A neurocomputational reservoir system can be used to alleviate these problems without training by enhancing the separability of regularities in input streams. However, as the regularities made separable vary with the state of the reservoir, feedback in the form of predictions of future sensory input can both enchance expected discriminations and hinder unanticipated ones. This renders the model blind to features not made separable in the regions of state space the reservoir in manipulated towards. This is demonstrated in a computational model of sustained inattentional blindness, leading to predictions about human behaviour that have yet to be tested.