This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directions for situation awareness research.

Methodologies for the empirical measurement of situation awareness are reviewed, including a discussion of the advantages and disadvantages of each method and the potential limitations of the measures from a theoretical and practical viewpoint. Two studies are presented that investigate questions of validity and intrusiveness regarding a query-based technique. This technique requires that a simulation of the operational tasks be momentarily interrupted in order to query operators on their situation awareness. The results of the two studies indicate that the query technique is not intrusive on normal subject behavior during the trial and does not suffer from limitations of human memory, which provides an indication of empirical validity. The results of other validity studies regarding the technique are discussed along with recommendations for its use in measuring situation awareness in varied settings.

Situation awareness (SA) is an important component of pilot/system performance in all types of aircraft. It is the role of the human factors engineer to develop aircraft cockpits which will enhance SA. Research in the area of situation awareness is is vitally needed if system designers are to meet the challenge of providing cockpits which enhance SA. This paper presents a discussion of the SA construct, important considerations facing designers of aircraft systems, and current research in the area of SA measurement.

The out-of-the-loop performance problem, a major potential consequence of automation, leaves operators of automated systems handicapped in their ability to take over manual operations in the event of automation failure. This is attributed to a possible loss of skills and of situation awareness (SA) arising from vigilance and complacency problems, a shift from active to passive information processing, and change in feedback provided to the operator. We studied the automation of a navigation task using an expert system and demonstrated that low SA corresponded with out-of-the-loop performance decrements in decision time following a failure of the expert system. Level of operator control in interacting with automation is a major factor in moderating this loss of SA. Results indicated that the shift from active to passive processing was most likely responsible for decreased SA under automated conditions.

Various levels of automation (LOA) designating the degree of human operator and computer control were explored within the context of a dynamic control task as a means of improving overall human/machine performance. Automated systems have traditionally been explored as binary function allocations; either the human or the machine is assigned to a given task. More recently, intermediary levels of automation have been discussed as a means of maintaining operator involvement in system performance, leading to improvements in situation awareness and reductions in out-of-the-loop performance problems. A LOA taxonomy applicable to a wide range of psychomotor and cognitive tasks is presented here. The taxonomy comprises various schemes of generic control system function allocations. The functions allocated to a human operator and/or computer included monitoring displays, generating processing options, selecting an`optimal' option and implementing that option. The impact of the LOA taxonomy was assessed within a dynamic and complex cognitive control task by measuring its eOE ect on human/system performance, situation awareness and workload. Thirty subjects performed simulation trials involving various levels of automation. Several automation failures occurred and out-of-the-loop performance decrements were assessed. Results suggest that, in terms of performance, human operators bene® t most from automation of the implementation portion of the task, but only under normal operating conditions; in contrast, removal of the operator from task implementation is detrimental to performance recovery if the automated system fails. Joint human/system option generation signi® cantly degraded performance in comparison to human or automated option generation alone. Lower operator workload and higher situation awareness were observed under automation of the decision making portion of the task (i.e. selection of options), although human/system performance was only slightly improved. The implications of these ® ndings for the design of automated systems are discussed.

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This paper extends previous research on two approaches to human-centred automation: (1) intermediate levels of automation (LOAs) for maintaining operator involvement in complex systems control and facilitating situation awareness; and (2) adaptive automation (AA) for managing operator workload through dynamic control allocations between the human and machine over time. Some empirical research has been conducted to examine LOA and AA independently, with the objective of detailing a theory of human-centred automation. Unfortunately, no previous work has studied the interaction of these two approaches, nor has any research attempted to systematically determine which LOAs should be used in adaptive systems and how certain types of dynamic function allocations should be scheduled over time. The present research briefly reviews the theory of humancentred automation and LOA and AA approaches. Building on this background, an initial study was presented that attempts to address the conjuncture of these two approaches to human-centred automation. An experiment was conducted in which a dual-task scenario was used to assess the performance, SA and workload effects of low, intermediate and high LOAs, which were dynamically allocated (as part of an AA strategy) during manual system control for various cycle times comprising 20, 40 and 60% of task time. The LOA and automation allocation cycle time (AACT) combinations were compared to completely manual control and fully automated control of a dynamic control task performed in conjunction with an embedded secondary monitoring task. Results revealed LOA to be the driving factor in determining primary task performance and SA. Low-level automation produced superior performance and intermediate LOAs facilitated higher SA, but this was not associated with improved performance or reduced workload. The AACT was the driving factor in perceptions of primary task workload and secondary task performance. When a greater percentage of primary task time was automated, operator perceptual resources were freed-up and monitoring performance on the secondary task improved. Longer automation cycle times than have previously been studied may have benefits for overall human-machine system performance. The combined effect of LOA and AA on all measures did not appear to be 'additive' in nature. That is, the LOA producing the best performance (low level automation) did not do so at the AACT, which produced superior performance (maximum cycle time). In general, the results are supportive of intermediate LOAs and AA as approaches to human-centred automation, but each appears to provide different benefits to human-machine system performance. This work provides additional information for a developing theory of human-centred automation.

Situation awareness (SA) has become a widely used construct within the human factors community, the focus of considerable research over the past 25 years. This research has been used to drive the development of advanced information displays, the design of automated systems, information fusion algorithms, and new training approaches for improving SA in individuals and teams. In recent years, a number of papers criticized the Endsley model of SA on various grounds. I review those criticisms here and show them to be based on misunderstandings of the model. I also review several new models of SA, including situated SA, distributed SA, and sensemaking, in light of this discussion and show how they compare to existing models of SA in individuals and teams.