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Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. We test the model on adoi:10.31234/osf.io/mbe5a fatcat:awvgu2zwc5attfoxmkokxvlrli