Abstract
In complex human-machine systems, the human operator is often required to intervene to detect and solve problems. Given this increased reliance on the human in these critical human-machine systems, there is an increasing need to validly predict how operators allocate their visual attention. This paper describes the information-seeking (attention-guiding) model within the Man-machine Integration Design and Analysis System (MIDAS) v5 software - a predictive model that uses the Salience, Effort, Expectancy and Value (SEEV) of an area of interest to guide a person’s attention. The paper highlights the differences between using a probabilistic fixation approach and the SEEV approach in MIDAS to drive attention.
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Gore, B.F., Hooey, B.L., Wickens, C.D., Scott-Nash, S. (2009). A Computational Implementation of a Human Attention Guiding Mechanism in MIDAS v5. In: Duffy, V.G. (eds) Digital Human Modeling. ICDHM 2009. Lecture Notes in Computer Science, vol 5620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02809-0_26
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DOI: https://doi.org/10.1007/978-3-642-02809-0_26
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