This work is in contrast to the efforts on IMPRINT, in which the focus is on behavior modification at the task level.
Although data exist on the effects of extrinsic moderator variables on human performance, most of these data provide only cutoff points or limits, rather than performance degradation functions. As a result, these variables cannot be encoded directly into models of human behavior. However, as shown by the work on IMPRINT, it is possible to develop and test hypotheses about the effects on performance time and accuracy of specific levels of such variables as temperature, noise, and sleepless hours.
With regard to internal moderators of behavior, several theories have been developed that categorize the variables related to personality, emotion, and cultural values. In some cases, the theories associate the variables with variations in performance. Yet there is a great deal of overlap among the variables classified under personality, emotion, attitude, and cultural values, and most of the empirical data in this area are subjective and qualitative. Some preliminary success has been achieved with the introduction of internal behavior moderators into models of human behavior. However, these models include extremely simplistic representations of the relationships between specified levels of one or more of these variables and performance.
Apply existing knowledge about both extrinsic and internal behavior moderators to establish value settings for various parameters of human behavior. That is, estimate how specified levels of sleep loss or fatigue, for example, might affect attention, multitasking, or decision making, and observe the effects of the use of such estimates in a sample of simulated engagements.
Study the effects of introducing emotion into models of human behavior in the form of relatively simple algorithms. For example, set up demonstrations to show how different levels and types of emotion affect decision making outcomes in the models. Use subjective assessments by experienced operational personnel to determine the value of these additions for increasing the realism of event sequences.
Formulate a research strategy for developing sufficient knowledge about selected behavior moderators to provide a basis for encoding these variables into computational models.
Develop computational models of behavior moderators that are based solidly on empirical research findings.