Some of the architectures, such as EPIC and MIDAS, have fairly detailed submodels of human sensory and perceptual processes. Such submodels make these architectures particularly applicable in situations where failures in stimulus detection or discrimination are likely to have a significant effect on overall human performance. Other architectures, such as Micro Saint, model such processes only in terms of times and accuracy probabilities, and still others, such as COGNET, have only very abstract representations of such processes, though they may be perfectly acceptable for applications in which detection and discrimination are not limiting factors. Even in cases in which detection and discrimination are limiting factors, most of the architectures offer modelers the capability to incorporate more sophisticated sensing and perception modules.
As discussed in Chapter 5, working or short-term memory plays a major role in human information processing. Applications in which attention and decision making are important considerations are likely to benefit from veridical representations of working memory. The most sophisticated working memory submodels among the architectures reviewed by the panel are those of ACT-R and the newer version of MIDAS, both of which treat working memory as an area of activation of long-term memory. Other architectures, such as Micro Saint, do not model working memory explicitly, and are therefore incapable of directly capturing performance degradation due to working memory capacity and duration limitations.
All the architectures provide some means of long-term information storage. Some, however, such as HOS and Micro Saint, do not explicitly model such a store. Rather, such information is implicitly stored in procedures and task networks, respectively.
The architectures reflect a wide range of motor submodels. The motor submodels of HOS and Micro Saint provide time and accuracy outputs. EPIC, ACT-R, and Soar have effectors that change the environment directly. MIDAS and OMAR drive the Jack animated mannequin, though the computational costs of that submodel may be too great for most simulations. OMAR has also developed