Attention can be biased by previous learning and experience. We present analgorithmic-level model of this bias in visual attention that predicts quantitatively howbottom-up, top-down and selection history compete to control attention. In the model,the output of saliency maps as bottom-up guidance interacts with a history map thatencodes learning effects and a top-down task control to prioritize visual features. Wetest the model on a reaction-time (RT) data set from the experiment presented in [1].The model accurately predicts parameters of reaction time distributions from anintegrated priority map that is comprised of an optimal, weighted combination ofseparate maps. Analysis of the weights confirms learning history effects on attentionguidance. The model is able to capture individual differences between participants.Moreover, we demonstrate that a model with a reduced set of maps performs worse,indicating that integrating history, saliency and task information are required for aquantitative description of human attention.
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