Attention to a spatial location or feature in a visual scene can modulate the responses of cortical neurons and affect perceptual biases in illusions. We add attention to a cortical model of spatial context based on a well-founded account of natural scene statistics. The cortical model amounts to a generalized form of divisive normalization, in which the surround is in the normalization pool of the center target only if they are considered statistically dependent. Here we propose that attention influences this computation by accentuating the neural unit activations at the attended location, and that the amount of attentional influence of the surround on the center thus depends on whether center and surround are deemed in the same normalization pool. The resulting form of model extends a recent divisive normalization model of attention (Reynolds & Heeger, 2009). We simulate cortical surround orientation experiments with attention and show that the flexible model is suitable for capturing additional data and makes nontrivial testable predictions.