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These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.

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Interruptions were studied extensively in the past but with a focus on their negative effects. Although many types of interruptions result in a break-in-task, in some cases interruptions communicate important information associated with patient's safety. The majority of previous interruption research use a reductionist approach to minimize or prevent interruptions, and minimal attention has been given to the differentiation between positive and negative interruptions. Through the analysis of relevant healthcare literature, this paper first identifies the inconsistencies in the way interruptions are defined, and then categorizes potential sources of negative and positive interruptions.

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Abstract-Discrete event simulations for futuristic unmanned vehicle (UV) systems enable a cost and time effective methodology for evaluating various autonomy and humanautomation design parameters. Operator mental workload is an important factor to consider in such models. We present that the effects of operator workload on system performance can be modeled in such a simulation environment through a quantitative relation between operator attention and utilization, i.e., operator busy time used as a surrogate real-time workload measure. In order to validate our model, a heterogeneous UV simulation experiment was conducted with 74 participants. Performancebased measures of attention switching delays were incorporated in the discrete event simulation model via UV wait times due to operator attention inefficiencies (WTAI). Experimental results showed that WTAI is significantly associated with operator utilization (UT), such that high UT levels correspond to higher wait times. The inclusion of this empirical UT-WTAI relation in the discrete event simulation model of multiple UV supervisory control resulted in more accurate replications of data, as well as more accurate predictions for alternative UV team structures. These results have implications for the design of future human-UV systems, as well as more general multiple task supervisory control models.

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