Model-based Prediction of Exogeneous and Endogeneous Attention Shifts During an Everyday Activity

Human attention determines to a large degree how users interact with technical devices and how technical artifacts can support them optimally during their tasks. Attention shifts between different targets, triggered through changing requirements of an ongoing task or through salient distractions in the environment. Such shifts mark important transition points which an intelligent system needs to predict and attribute to an endogenous or exogenous cause for an appropriate reaction. In this paper, we describe a model which performs this task through a combination of bottom-up and topdown modeling components. We evaluate the model in a scenario with a dynamic task in a rich environment and show that the model is able to predict attention future switches with a robust classification performance.