as such, perceptions of reliability, in general, can result in a form of automation irony, in which more automation can decrease cognitive workload but increase the opportunity for monitoring errors. in contrast, low automation can increase workload but decrease the opportunity for monitoring errors. take, for example, a pilot flying through inclement weather, in which continuous thunder interferes with the pilot's ability to understand information transmitted by an air traffic controller (atc). despite how much effort is allocated to understanding information transmitted by atc, the pilot's performance is limited by the source of information needed for the task. the pilot therefore has to rely on automated gauges in the cockpit to understand flight path information. if the pilot perceives the automated gauges to be highly reliable, the amount of effort needed to understand atc and automated gauges may decrease. moreover, if the automated gauges are perceived to be highly reliable, the pilot may ignore those gauges to devote mental resources for deciphering information transmitted by atc. in so doing, the pilot becomes a complacent monitor, thereby running the risk of missing critical information conveyed by the automated gauges. if, however, the pilot perceives the automated gauges to be unreliable, the pilot will now have to interpret information from atc and automated gauges simultaneously. this creates scenarios in which the operator may be expending unnecessary cognitive resources when the automation is in fact reliable, but also increasing the odds of identifying potential errors in the weather gauges should they occur. to calibrate the pilot's perception of reliability, automation should be designed to maintain workload at appropriate levels while also ensuring the operator remains engaged with monitoring tasks. the operator should be less likely to disengage from monitoring when the system's reliability can change as compared to a system that has consistent reliability (parasuraman, 1993).