System Monitoring Task Research Articles

The effects of personality on supervisory control tasks

Personality is a collection of holistic human traits: character, perception of the world, covert and overt behaviors, uniqueness in functioning and coping within cultural setting, and response to different environmental stimuli. While many studies have focused on perception and cognitive aspects of supervisory control (SC), not much consideration is given to the effect of personality on supervisory control performance. In this paper, results showing the effects of Type A and Type B personality types on SC and workload perception are discussed. One major finding of the study shows that Type A individuals respond better than Type B to monitoring single tasks for automation failure, and they also experience the least amount of workload in both single and multiple SC tasks. However, Type A individuals made significantly more errors, than Type B individuals, during system monitoring and resource management tasks.

Automation-induced monitoring inefficiency: role of display location

Operators can be poor monitors of automation if they are engaged concurrently in other tasks. However, in previous studies of this phenomenon the automated task was always presented in the periphery, away from the primary manual tasks that were centrally displayed. In this study we examined whether centrally locating an automated task would boost monitoring performance during a flight-simulation task consisting of system monitoring, tracking and fuel resource management sub-tasks. Twelve nonpilot subjects were required to perform the tracking and fuel management tasks manually while watching the automated system monitoring task for occasional failures. The automation reliability was constant at 87.5% for six subjects and variable (alternating between 87.5% and 56.25%) for the other six subjects. Each subject completed four 30 min sessions over a period of 2 days. In each automation reliability condition the automation routine was disabled for the last 20 min of the fourth session in order to simulate catastrophic automation failure (0% reliability). Monitoring for automation failure was inefficient when automation reliability was constant but not when it varied over time, replicating previous results. Furthermore, there was no evidence of resource or speed accuracy trade-off between tasks. Thus, automation-induced failures of monitoring cannot be prevented by centrally locating the automated task.

The effect of prolonged sitting on mental task performance

Due to changes in technology, people are frequently relegated to the role of system monitor. This has created a more sedentary workplace, where the person commonly assumes a traditional seated position for prolonged periods of time. The seated position causes both biomechanical and physiologic stressors which can lead to discomfort over a prolonged period. It was hypothesized that the discomfort associated with prolonged sitting will degrade performance of a system monitoring task. A pilot study was conducted testing this hypothesis.

Cumulative Effects of +Gz on Cognitive Performance

A study is described which examines the interaction of two of the most salient stressors found in the tactical aviation cockpit: 1) highly demanding decision-making tasks, and 2) intermittent periods of high G. Addressing this issue is critical because: 1) it is probable that physiological stressors such as G may have serious ramifications on pilots' abilities to perform complex cognitive tasks; 2) there may be different impacts of G on different types of cognitive tasks, and the impact of these deficits may be correlated with the cumulative amount of time a pilot has spent under G; 3) it is not clear that donning protective gear will have an effect on cognitive task performance; and 4) there are no data to suggest how long cognitive decrements due to exposure to physiological stressors will last. A general approach is described for evaluating the cumulative effects of physiological stressors (e.g., G) on cognitive task performance in a within-subjects experimental design. A PC-based task suite was used incorporating three concurrently performed tasks: a compensatory tracking task, a resource management task, and a system monitoring task. Results indicated that performance across the experimental conditions was highly variable. Preliminary results demonstrated that: 1) even low-level increases in G can be disruptive to subject performance, 2) there are cumulative decrements in task performance during G, and 3) if stable performance on tasks is required, training for complex task performance must take place in the presence of salient physiological stressors.

Monitoring Automation Failures: Effects of Single and Multi-Adaptive Function Allocation

Adaptive function allocation has been proposed to allow the advantages of task automation to be realized without some of the disadvantages of “static” automation. However, few empirical studies of the efficacy of adaptive allocation have been reported. The effects of adaptive function allocation on monitoring for automation failure during multi-task flight simulation were examined in two experiments. The first study examined the use of two methods of adaptive function allocation as a possible counter-measure to automation-induced monitoring problems. Subjects were required to perform a tracking and fuel management task while monitoring an automated system monitoring task for possible failures. For the “model-based” and “performance-based” adaptive groups, a single 10-minute block of fully manual performance on the monitoring task was allocated to subjects in the middle of a session. For the control group system monitoring was automated throughout all sessions. All three groups had low probabilities of detection of automation failures for the first 40 minutes spent with automation, before the adaptive function allocation change. However, detection probabilities were higher for both adaptive groups than for the control group following the function allocation change. The second study found that multiple adaptive changes (repeated function allocation) sustained these performance benefits over a longer automation period. These results clearly indicate that adaptive function allocation can improve detection of automation failures and that the improvement can be sustained over long periods of automation cycles.

Performance Consequences of Automation-Induced 'Complacency'

The effect of variations in the reliability of an automated monitoring system on human operator detection of automation failures was examined in two experiments. For four 30-min sessions, 40 subjects performed an IBM PC-based flight simulation that included manual tracking and fuel-management tasks, as well as a system-monitoring task that was under automation control. Automation reliability - the percentage of system malfunctions detected by the automation routine - either remained constant at a low or high level over time or alternated every 10 min from low to high. Operator detection of automation failures was substantially worse for constant-reliability than for variable-reliability automation after about 20 min under automation control, indicating that the former condition induced 'complacency'. When system monitoring was the only task, detection was very efficient and was unaffected by variations in automation reliability. The results provide the first empirical evidence of the performance consequences of automation-induced 'complacency'. We relate findings to operator attitudes toward automation and discuss implications for cockpit automation design.

Measurement Based Control of Power Systems

Publisher Summary This chapter provides an overview of measurement-based control of power systems. The measurements are generally of fundamental frequency components of the signals in the presence of extraneous components in the power system voltage and current signals. Although measurements at such high speed are necessary in relaying applications, slower measurements have better accuracy and are therefore more desirable for power system monitoring and control tasks. As the measurements provide symmetrical components of voltages and currents, positive sequence quantities can be used in the monitoring and control tasks. The power system models used in these applications is the positive sequence model; it is therefore desirable that a consistent measurement set be used. The phasor representation of power system voltages and currents can be viewed as the Discrete Fourier Transform (DFT) of their data samples. Phasor calculations can be performed over one period, or over a fraction of a period. Longer the data window used for phasor calculation, better is the accuracy of its estimate. If the power system frequency changes from its nominal value, the phasor rotates in the complex plane at a speed proportional to the shift in frequency. Consequently, the recursive DFT offers a very sensitive and accurate frequency measuring system. There are three practical alternative techniques for synchronization of clocks to this accuracy: the first one is the navigational-aid transmission, the second alternative is the use of satellite transmissions, and the third alternative is the use of a direct communication channel between substations—preferably a fiber-optic channel.

The Allocation of Attentional Resources in a Dynamic Environment

Three experiments are reported in which subjects performed a primary task, whose difficulty varied quasi-randomly over time, concurrently with a secondary task, whose difficulty remained constant. Three models of resource allocation were described which varied in the degree of optimality with which operators can mobilize resources in response to the demand changes. These are quantified by linear time-series analysis. In experiment 1 employing two tracking tasks, allocation behavior was non-optimal, but indicated a trend toward optimality with practice. In experiment 2 when tracking difficulty was varied concurrently with a dynamic system monitoring task, behavior was somewhat optimal. In experiment 3 which again paired tracking and monitoring, but varied monitoring task demand, resource allocation was extremely non-optimal. Some reasons for this departure from optimality are considered.