The conceptual design of a comprehensive support system for operators of complex systems is presented. Key functions within the support system architecture include information management, error monitoring, and adaptive aiding. One of the central knowledge sources underlying this functionality is an operator model that involves a combination of algorithmic and symbolic models for assessing and predicting an operator's activities, awareness, intentions, resources, and performance. Functional block diagrams are presented for the overall architecture as well as the key elements within this architecture. A variety of difficult design issues are discussed, and ongoing efforts aimed at resolving these issues are noted.

An investigation of the behavior of the human decisionmaker is described for a task related to the problem of a pilot using a traffic situation display to avoid collisions. This sequential signal detection task is characterized by highly correlated signals with time varying strength. Experimental results are presented and the behavior of the observers is analyzed using the theory of Markov processes and classical signal detection theory. Mathematical models are developed which describe the main result of the experiment: that correlation in sequential signals induced perseveration in the observer response and a strong tendency to repeat their previous decision, even when they were wrong.

The effects of gust disturbances and the pilot's participation mode in the control task on his work-load level and failure detection performance during a simulated low visibility landing approach are examined. We found that the participation mode had a strong effect on the pilot's work load, the induced work load being lowest when the pilot acted as a monitoring element during a coupled approach and highest when the pilot was an active element in the control loop. The effects of differential work load and participation mode on failure detection were separated. The participation mode was shown to have a dominant effect on the failure detection performance, with a failure in a monitored (coupled) axis being detected significantly faster than a comparable failure in a manually controlled axis.

Control theoretic techniques are applied to develop two new models for predicting human operator performance when monitoring an automatically controlled system. In one case it is assumed that the human monitors the instruments in order to rapidly detect failures. A second approach assumes that the instruments are sampled to best reconstruct the system status information. The relation of these models to existing prediction schemes, e.g., equal attention and the Senders model is explored. It is concluded that a combination of failure detection and status estimation models offers the best potential for human operator application.

A model for the human observer in failure detection tasks is proposed which consists of two stages: a linear estimator and a decision mechanism. The estimator is a Kalman filter, and the decision mechanism, which is based on Wald's sequential analysis, leads to a decision function which is the integration of the filter residuals. The final result is a simple detection system which depends on only three parameters, and the sensitivity of the model to these parameters is analyzed. The results of an experiment designed to test the validity of the model are reported. The question of open and closed decision intervals as well as the generalization of the model to more complicated cases is discussed.

Statistics conditioned on quantized measurements are considered in the general case. These results are specialized to Gaussian parameters and then extended to discrete-time linear systems. The conditional mean of the system's state vector may be found by passing the conditional mean of the measurement history through the Kalman filter that would be used had the measurements been linear. Repetitive use of Bayes' rule is not required. Because the implementation of this result requires lengthy numerical quadrature, two approximations are considered: the first is a power-series expansion of the probablity-density function; the second is a discrete-time version of a previously proposed algorithm that assumes the conditional distribution is normal. Both algorithms may be used with any memory length on stationary or nonstationary data. The two algorithms are applied to the noiseless-channel versions of the PCM, predictive quantization, and predictive-comparison data compression systems; ensemble-average performance estimates of the nonlinear filters are derived. Simulation results show that the performance estimates are quite accurate for most of the cases tested.