if so, when to start the descent. The model was exercised for a variety of scenarios. The experimenters then collected simulator data with four two-pilot crews. The behavior of the model was comparable to that of the human pilots (0.23 ≤ p ≤ 0.33) (Laughery and Corker, 1997).

Applicability for Military Simulations

In both versions, MIDAS is an integrative, versatile model with much (perhaps excess) detail. Its submodels are often based on current psychological and psychomotor theory and data. Its task loading model is consistent with multiple resource theory. MIDAS explicitly models communication, especially in the new version. Much modeling attention has been given to situation awareness with respect to the updatable world representation. There has been some validation of MIDAS.

On the other hand, MIDAS has some limitations. Many MIDAS behaviors, such as operator errors, are not emergent features of the model, but must be explicitly programmed. The Z-Scheduler is of dubious psychological validity. The scale-up of the original MIDAS to multioperator systems would appear to be quite difficult, though this problem is being addressed in the redesign effort. MIDAS is also too big and too slow for most military simulation applications. In addition, it is very labor-intensive, and it contains many details and features not needed in military simulations.

Nevertheless, MIDAS has a great deal of potential for use in military simulations. The MIDAS architecture (either the original version or the redesign) would provide a good base for a human behavior representation. Components of MIDAS could be used selectively and simplified to provide the level of detail and performance required. Furthermore, MIDAS would be a good testbed for behavioral representation research.

Neural Networks

In the past decade, great progress has been made in the development of general cognitive systems called artificial neural networks (Grossberg, 1988), connectionistic networks (Feldman and Ballard, 1982), or parallel distributed processing systems (Rumelhart et al., 1986b; McClelland and Rumelhart, 1986). The approach has been used to model a wide range of cognitive processes and is in widespread use in cognitive science. Yet neural network modeling appears to be quite different from the other architectures reviewed in this section in that it is more of a computational approach than an integrative human behavior architecture. In fact, the other architectures reviewed here could be implemented with neural networks: for example, Touretzky and Hinton (1988) implemented a simple production system and an experimental version of Soar was implemented in neural nets (Cho et al., 1991).

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