Cognitive Engineering and Naturalistic Decision Making are presented as two related fields of endeavor that seek to understand how people process information and perform within complex systems and to develop ways of applying this knowledge within the design and training process This panel presents an overview of the current state of the art in this research domain and charts paths for needed developments in the field in the near future.
Get full access to this article
View all access options for this article.
References
1.
DreyfusS. E. (1981). Formal models vs. human situational understanding: Inherent limitations on the modeling of business expertise (ORC 81–3). Berkeley: Operations Research Center, University of California
2.
HollnagelE.WoodsD. D. (1983). Cognitive systems engineering: New wine in new bottles. International Journal of Man-Machine Studies, 18, 583–591
3.
KleinG. A. (1989). Recognition-primed decisions. In RouseW. B. (Eds.), Advances in man-machine systems research (pp. 47–92). Greenwich, Conn: JAI Press, Inc
4.
KleinG. A. (1993). A recognition primed decision (RPD) model of rapid decision making. In KleinG. A.OrasanuJ.CalderwoodR.ZsambokC. E. (Eds.), Decision making in action: Models and methods (pp. 138–147). Norwood, NJ: Ablex
5.
KleinG. A.CalderwoodR.Clinton-CiroccoA. (1986). Rapid decision making on the fire ground. Proceedings of the Human Factors Society 30th Annual Meeting (pp. 576–580). Santa Monica, CA: Human Factors Society
6.
NormanD. A. (1981). Steps towards a cognitive engineering (Tech. Report). San Diego: University of California, Program in Cognitive Science
7.
NormanD. A. (1986). Cognitive Engineering. In NormanD. A.DraperS. W. (Eds.), User Centered System DesignHillsdale, NJ: Erlbaum
8.
WoodsD. D.RothE. M. (1988). Cognitive engineering: Human problem solving with tools. Human Factors, 30 (4), 415–430