Attention plays a fundamental role in higher-level cognition. In this paper we develop a computational model for how auditory spatial attention is distributed in space. Our model builds on the assumption that attentional bias has bottom-up and top-down components. We represent each component and their synthesis as a map, associating a level of attentional bias to locations in space. The maps and their interaction are modeled using an artificial intelligence approach based on constraints. We describe the behavioral task we have designed to measure the attentional bias and discuss the results. We then test different hypotheses on the shape and interaction modalities of the maps in terms of how well they fit our behavioral data. The findings showed that combining top-down and bottom-up spatial attention gradients that differ in their spatial properties produced the best fit to behavioral data, and suggested several novel mechanisms for future testing.