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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5620 LNCS, pp. 237–246). https://doi.org/10.1007/978-3-642-02809-0_26
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