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In this work, we present a novel probabilistic appearance representation and describe its application to surprise detection in the context of cognitive mobile robots. The luminance and chrominance of the environment are modeled by Gaussian distributions which are determined from the robot's observations using Bayesian inference. The parameters of the prior distributions over the mean and the precision of the Gaussian models are stored at a dense series of viewpoints along the robot's trajectory. Our probabilistic representation provides us with the expected appearance of the environment and enables the robot to reason about the uncertainty of the perceived luminance and chrominance. Hence, our representation provides a framework for the detection of surprising events, which facilitates attentional selection. In our experiments, we compare the proposed approach with surprise detection based on image differencing. We show that our surprise measure is a superior detector for novelty estimation compared to the measure provided by image differencing.