As the sophistication of computer technology increases, the number of systems in which both a human and a computer take controlling roles will increase. Effective communication between the human and computer is essential to prevent redundant or interfering actions. If a method for accurately predicting the human's actions can be determined, the computer can use the predictions to complement the human's actions rather than interfere with them. This correspondence develops and empirically validates a two-stage mathematical model of human performance in a situation with real time, multiple tasks, and process control. The model is a consistent and accurate predictor of human performance, correctly predicting over 80 percent of the subjects' actions over a range of situations. A subsequent study is suggested to assess the value of this model when the computer is implemented as a parallel decisionmaker, selecting its own actions to complement rather than conflict with the human decisionmaker.

When tasks are allocated dynamically within a human-computer system, system performance depends upon effective communication between the human and the computer. The results are presented of simulation studies which investigate two means of human-to-computer communication: implicit communication, in which the human's planned actions are conveyed to the computer through a model of the human's action strategy; and explicit communication, in which the human overtly transmits decisions to the computer. The results indicate that the effectiveness of implicit communication depends upon both the predictive validity of the model employed and the computer task allocation strategy built upon the model. The effectiveness of explicit communication depends upon the time the human must devote to transmitting action plans to the computer. These simulations indicate the payoffs that can be realized by optimization of implicit and explicit human-computer communication. They also provide an efficient means to determine the appropriate mix of these communication techniques for a given situation. Perhaps the most interesting finding is that implicit communication based upon a model possessing only modest predictive validity is capable of enhancing system performance. This suggests that implementation of implicit communication within human-computer systems need not require the development and use of complex all-encompassing models of human performance.

When a human and computer perform similar tasks in parallel, it is important that there be effective communication between the two entities. Because explicit communication may add to the human's workload, an implicit method of communication is suggested in which the computer references a model of human performance to predict the human's actions. The computer then determines its own actions so as to complement rather than conflict with the predicted human actions. A two-stage mathematical model of human performance is employed in an experimental situation in which both a human and computer act as decisionmakers. Implementation of the model significantly improves the human's performance, without degrading the computer's performance. Research into additional experimental and real-world situations is suggested.

When a human and computer perform similar tasks in parallel, it is important that an effective line of communication exist between the two entities. Since overt communication may add to the human's workload, an implicit method of communication is suggested in which the computer has a model of human performance on which to base actions. A two-stage model of human performance is employed in an experimental situation in which both a human and a computer act as decision makers. Results indicate that the implementation of a model significantly improves the human's performance and the overall system performance, without degrading the computer's performance. Research into additional experimental and real-world situations is suggested.