brief period of time, before returning it once again to automation. The benefits of temporary allocation of a task to human control may persist for some time, even after the task is returned to automation control. This hypothesis was tested in a study by Parasuraman, Mouloua, and Molloy (1996). During multiple-task flight simulation, a previously automated engine-status monitoring task was adaptively allocated to the operator for a 10-minute period in the middle of a session, and then returned to automatic control (see Figure 1.6).

Detection of engine malfunctions was better during the 10-minute block when the task was returned to human control from automation, consistent with previous reports of superior monitoring under conditions of active human control (Parasuraman et al., 1993; Wickens and Kessel, 1979). More importantly, however, detection performance under automation control was markedly superior in the post-allocation phase than in the identical pre-allocation phase (see Figure 1.6). (In both these phases, the engine-status monitoring task was automated but the post-allocation phase immediately followed one in which the task was performed manually.) The performance benefit (of about 66 percent) persisted even after the engine-status monitoring task was returned to automation, for about 20 minutes. The benefit of adaptive task allocation was attributed to this procedure, allowing human operators to update their memory of the engine-status monitoring task. A similar view was put forward by Lewandowsky and Nikolic (1995) on the basis of a connectionist (neural network) simulation of these monitoring performance data.

In addition to improved monitoring, benefits of adaptive automation for operator mental workload have also been reported in recent studies by Hilburn (1996). This research is of particular interest because it examined the utility of adaptive automation in the specific context of air traffic control. Experienced controllers worked with an advanced simulation facility, NARSIM, coupled with the CTAS automation tool, specifically the descent advisor (DA). Controllers were required to perform the role of an executive controller in a southern sector of the Amsterdam airspace. A plan view display contained traffic with associated flight data blocks, a data link status panel, and the descent advisor timeline display from CTAS. Three levels of CTAS automated assistance could be provided: none (manual, traffic status only), conflict detection only, or conflict detection plus resolution advisory. Controller workload was assessed using physiological (eye scan entropy, heart rate variability) and subjective measures (NASA-TLX). Monitoring was assessed by recording controller reaction times to respond to occasional data link anomalies. A baseline study established that controller workload increased with traffic load but was reduced by each level of automation assistance compared with manual performance.

In a second study, Hilburn (1996) examined the effects of adaptive automation for two levels of CTAS aiding: manual control or resolution advisory. In two static automation conditions, automation level remained constant throughout the simulation, irrespective of shifts in traffic load. In the adaptive condition,

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