In this paper, I present a framework which considers three independent factors that drive attentional selection. In addition to goal-driven and stimulus-driven selection, I discuss how lingering biases of selection history play a major role in attentional selection. Visual statistical learning of the regularities in the environment forms the basis for this history-based selection which provides an elaborate and flexible attentional biasing mechanism above and beyond goal-driven and stimulus-driven factors. A selection based on experience and history is fast, automatic and occurs without much, if any, effort. I conclude that learning and extracting the distributional properties of the environment have a major impact on attentional selection.