Abstract
A model of the human decision maker observing a dynamic system is presented. The decision process is described in terms of classical sequential decision theory by considering the hypothesis that an abnormal condition has occurred by means of a generalized likelihood ratio test. For this, a sufficient statistic is provided by the innovation sequence which is the result of the perception and information processing submodel of the human observer. On the basis of only two model parameters the model predicts the decision speed/accuracy trade-off and various attentional characteristics. A preliminary test of the model for single variable failure detection tasks resulted in a very good fit of the experimental data. In a formal validation programme a variety of multivariable failure detection tasks was investigated. A very good overall agreement between the model and experimental results showed the predictive capability of the model. In addition, the specific effect of almost all task variables (number, bandwidth and mutual correlation of display variables and various failure characteristics) was accurately predicted by the model.
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