Automation Technology and Human Performance: Current Research and Trends

By Mark W. Scerbo | Go to book overview

would add a dimension of experimental control, since the behavior of the agent would be fully controllable, or at least controllable within known limits. If one is to study such environments experimentally, a synthetic environment of this type needs to be created in a controlled way. In developing such simulation environments one of the primary capabilities to establish is that of the problem space or the framework from which problem information, data, and parameters evolve. Some call this type of capability a scenario generator, in which simulated observable cues are produced as inputs to processes under study. In our case, we are proposing the use of an existing capability for this, the so-called "SAGE" (Semi-Automated Ground Environment) software system. SAGE can also provide representation of two adversarial commanders, or at least the information environments in which their decision-making is being conducted.


SUMMARY AND FUTURE RESEARCH

Applicability of the Sheridan's classification into AADM systems has been investigated. Because the classification offers a variety of characteristics of trust in supervisory control systems, it forms a good basis for the conceptualization of trust in AADM environment and further development of experimental framework. Due to the particular characteristic of the domain which adversaries, and multiple processes are involved, however, the experimental framework should consider the possible points of attack which could be varied from the real world to human operators. Based on the interpretation of the classification from the AADM perspective, then, three factors, and scenarios are discussed for an experimental framework in AADM environment. These factors include the dimension of the point of attack, degree of attack, and position of attack which may affect human operator's role of monitoring activities and decision-making actions, which consequently may have an impact on operators' calibration of trust.


ACKNOWLEDGMENTS

This work was supported by the Air Force Armstrong Laboratory; the support of Mr. Gil Kupperman is gratefully acknowledged. SAGE was made available by Ball Aerospace Corp., whose support is gratefully acknowledged.


REFERENCES

Cooksey R. W. ( 1996). Judgment analysis: Theory, methods and applications, New York: Academic Press.

Klein G. ( 1997). The Recognition-Primed Decision (RPD) Model: Looking back, Looking forward. In C. E. Zsambok and G. Klein (Eds.), Naturalistic decision making (pp. 285-292). Mahwah, NJ: Lawrence Erlbaum.

Lee J. D., & Moray N. ( 1992). Trust, control strategies and allocation of function in human-machine systems. Ergonomics, 35( 10), 1243-1270.

Lee J. D., & Moray N. ( 1994). Trust, self-confidence, and operators' adaptation to automation. International Journal of Human-Computer Studies, 40, 153-184.

Muir B. M., & Moray N. ( 1996). Trust in automation: Part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics, 39( 3), 429-460.

Pulford B. D., Colman A. M. ( 1996). Overconfidence, base rates and outcome positivity/negativity of predicted events. British Journal of Psychology, 87( 3), 431-445.

Sheridan T. B. ( 1980). Computer control and human alienation. Technology Review, October, 61-73.

Tversky A., & Kahneman D. ( 1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124-1131.

Wickens C. ( 1992). Engineering psychology and human performance. 2nd Ed., New York: Harper-Collins.

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