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Situational awareness or situation awareness (SA) is the perception of environmental elements and events with respect to time or space, the comprehension of their meaning, and the projection of their future status. 
Situation awareness has been recognized as a critical, yet often elusive, foundation for successful decision-making across a broad range of situations, including aviation, air traffic control, ship navigation, health care, emergency response, military command and control operations, and offshore oil and nuclear power plant management. Lacking or inadequate situation awareness has been identified as one of the primary factors in accidents attributed to human error.
The formal definition of SA is broken down into three segments: perception of the elements in the environment, comprehension of the situation, and projection of future status. Three facets of SA have been in focus in research: SA states, SA systems, and SA processes. SA states refers to the actual awareness of the situation. SA systems refers to the distribution of SA in teams and between objects in the environment, and to the exchange of SA between system parts. SA processes refers to the updating of SA states, and what guides the moment-to-moment change of SA.
- 1 History
- 2 Related concepts
- 3 Theoretical model
- 4 In team operations
- 5 In time critical decision-making processes
- 6 Measurement
- 7 Training
- 8 On-the-job examples
- 9 Situation awareness-based agency transparency (SAT) model
- 10 Methods of gaining situational awareness
- 11 See also
- 12 References
Although the term itself is fairly recent, the concept has roots in the history of military theory—it is recognizable in Sun Tzu's The Art of War, for example. The term can be traced to World War I, where it was recognized as a crucial skill for crews in military aircraft.
There is evidence that the term situational awareness was first employed at the Douglas Aircraft Company during human factors engineering research while developing vertical and horizontal situation displays and evaluating digital-control placement for the next generation of commercial aircraft. Research programs in flight-crew computer interaction and mental workload measurement  built on the concept of awareness measurement from a series of experiments that measured contingency awareness during learning, and later extended to mental workload and fatigue.
Situation awareness appears in the technical literature as early as 1983, when describing the benefits of a prototype touch-screen navigation display. During the early 1980s, integrated “vertical-situation” and “horizontal-situation” displays were being developed for commercial aircraft to replace multiple electro-mechanical instruments. Integrated situation displays combined the information from several instruments enabling more efficient access to critical flight parameters, thereby improving situational awareness and reducing pilot workload.
Before being widely adopted by human factors scientists in the 1990s, the term is said to have been used by United States Air Force (USAF) fighter aircrew returning from war in Korea and Vietnam. They identified having good SA as the decisive factor in air combat engagements—the "ace factor". Survival in a dogfight was typically a matter of observing the opponent's current move and anticipating his next move a fraction of a second before he could observe and anticipate it himself. USAF pilots also came to equate SA with the "observe" and "orient" phases of the famous observe-orient-decide-act loop (OODA loop), or Boyd cycle, as described by the USAF war theorist Col. John Boyd. In combat, the winning strategy is to "get inside" your opponent's OODA loop, not just by making one's own decisions quicker, but also by having better SA than one's opponent, and even changing the situation in ways that the opponent cannot monitor or even comprehend. Losing one's own SA, in contrast, equates to being "out of the loop".
Clearly, SA has far reaching applications, as it is necessary for individuals and teams to function effectively in their environment. Thus, we are beginning to see SA going beyond the field of aviation to work being conducted in a wide variety of environments. Currently, SA is being studied in such diverse areas as air traffic control, nuclear power plant operation, vehicle operation, and anesthesiology.
Several cognitive processes related to situation awareness are briefly described in this section. The matrix shown below attempts to illustrate the relationship among some of these concepts. Note that situation awareness and situational assessment are more commonly discussed in information fusion complex domains such as aviation and military operations and relate more to achieving immediate tactical objectives. Sensemaking and achieving understanding are more commonly found in industry and the organizational psychology literature and often relate to achieving long-term strategic objectives.
|Objective||Tactical (short-term)||situational assessment||situation awareness|
Situation awareness is sometimes confused with the term "situational understanding." In the context of military command and control applications, situational understanding refers to the "product of applying analysis and judgment to the unit's situation awareness to determine the relationships of the factors present and form logical conclusions concerning threats to the force or mission accomplishment, opportunities for mission accomplishment, and gaps in information". Situational understanding is the same as Level 2 SA in the Endsley model—the comprehension of the meaning of the information as integrated with each other and in terms of the individual's goals. It is the "so what" of the data that is perceived.
In brief, situation awareness is viewed as "a state of knowledge," and situational assessment as "the processes" used to achieve that knowledge. Endsley argues that "it is important to distinguish the term situation awareness, as a state of knowledge, from the processes used to achieve that state. These processes, which may vary widely among individuals and contexts, will be referred to as situational assessment or the process of achieving, acquiring, or maintaining SA." Note that SA is not only produced by the processes of situational assessment, it also drives those same processes in a recurrent fashion. For example, one's current awareness can determine what one pays attention to next and how one interprets the information perceived.
Accurate mental models are one of the prerequisites for achieving SA. A mental model can be described as a set of well-defined, highly organized yet dynamic knowledge structures developed over time from experience. The volume of available data inherent in complex operational environments can overwhelm the capability of novice decision makers to attend, process, and integrate this information efficiently, resulting in information overload and negatively impacting their SA. In contrast, experienced decision makers assess and interpret the current situation (Level 1 and 2 SA) and select an appropriate action based on conceptual patterns stored in their long-term memory as "mental models". Cues in the environment activate these mental models, which in turn guide their decision making process.
Klein, Moon, and Hoffman distinguish between situation awareness and sensemaking as follows:
...situation awareness is about the knowledge state that's achieved—either knowledge of current data elements, or inferences drawn from these data, or predictions that can be made using these inferences. In contrast, sensemaking is about the process of achieving these kinds of outcomes, the strategies, and the barriers encountered.
In brief, sensemaking is viewed more as "a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively", rather than the state of knowledge underlying situation awareness. Endsley points out that as an effortful process, sensemaking is actually considering a subset of the processes used to maintain situation awareness. In the vast majority of the cases, SA is instantaneous and effortless, proceeding from pattern recognition of key factors in the environment—"The speed of operations in activities such as sports, driving, flying and air traffic control practically prohibits such conscious deliberation in the majority of cases, but rather reserves it for the exceptions." Endsley also points out that sensemaking is backward focused, forming reasons for past events, while situation awareness is typically forward looking, projecting what is likely to happen in order to inform effective decision processes.
SA can be described in terms of a holistic framework of SA systems, states, and processes. SA descriptions usually focus on one of the three aspects, or on combinations. SA states can be described as:
Objects: Awareness of various objects in the world, and their current status. Objects and their status may be indicative of particular situations (that they are about to occur, that they are ongoing, etc.). Then they are often referred to as cues.
Frames: Awareness of what kind of situation is on-going, e.g. a runway incursion where an aircraft is about to collide with some object on the runway.
Implications: Awareness of objects within frames, of what their current status means in a particular situation. E.g. the implications of the current speed of the aircraft, and the distance to an object on the runway, in a runway incursion situation. The implications refer to time and space, to an event horizon.
Event horizon: An awareness of plans and events in time and space. It includes an awareness of what has happened (useful for diagnosis, to achieve SA, to frame situations). It also includes prognosis, an awareness of what might happen next. That includes on the one hand an awareness both of what might occur based on diagnosis and the current situation, and on the other hand on an awareness of current plans and intentions.
All four aspects may drive SA processes. Being aware of the status of particular objects (cues), one might infer that particular situations are on-going, and frame the objects accordingly. The cues then drive re-framing of situations. Having a particular frame, or pre-conception of a situation, this may drive the perception of objects. E.g. having noticed that a landing is about to occur, an Air Traffic Controller will usually look for specific objects in the environment and update the awareness of their status. Further, having realized the implications of objects of their status, this drives the process of what to attend to next. E.g. knowing that a vehicle is approaching a runway where a landing is about to take place, an Air Traffic Controller may monitor its progress. Event horizon awareness may also guide SA, e.g. if one plans to stop the car at a gas station, one may look for gas station signs.
Further, to describe SA in e.g. teams, the distribution of SA must be considered, e.g. in terms of:
Shared SA: What SA different agents have in common
Task SA: What SA different agents have, that they need to perform their tasks
Transactive SA: Exchange of SA between system parts
Buffering SA: Awareness of different accounts (e.g., different frames) of situations, in various parts of the system.
Causal models of SAEdit
A recent holistic framework has shown how SA emerges from causal mental models, where beliefs about causal relationships in the world are represented in the structure of the model and beliefs about the state of the world are reflected in the state of the model. Causal models support inferences about causes and consequences, as well as counterfactual thinking and allow us to ask questions of why?, so-what?, and what-if?, that support SA comprehension and projection as well as naturalistic decision-making. When instantiated as a Bayesian network, these models also allow evaluation of the search for information and the integration of new information with existing knowledge. A computational causal model quantitatively predicted search behavior and changes in SA in fighter pilots.
The SA states framework above extends an older (but simpler), theoretical framework of SA, provided by Dr. Mica Endsley (1995b), which has historically been widely used. Endsley's model describes SA states, and illustrates three stages or steps of SA formation: perception, comprehension, and projection.
Perception (Level 1 SA): The first step in achieving SA is to perceive the status, attributes, and dynamics of relevant elements in the environment. Thus, Level 1 SA, the most basic level of SA, involves the processes of monitoring, cue detection, and simple recognition, which lead to an awareness of multiple situational elements (objects, events, people, systems, environmental factors) and their current states (locations, conditions, modes, actions).
Comprehension (Level 2 SA): The next step in SA formation involves a synthesis of disjointed Level 1 SA elements through the processes of pattern recognition, interpretation, and evaluation. Level 2 SA requires integrating this information to understand how it will impact upon the individual's goals and objectives. This includes developing a comprehensive picture of the world, or of that portion of the world of concern to the individual.
Projection (Level 3 SA): The third and highest level of SA involves the ability to project the future actions of the elements in the environment. Level 3 SA is achieved through knowledge of the status and dynamics of the elements and comprehension of the situation (Levels 1 and 2 SA), and then extrapolating this information forward in time to determine how it will affect future states of the operational environment.
Endsley's model of SA also illustrates several variables that can influence the development and maintenance of SA, including individual, task, and environmental factors. For example, individuals vary in their ability to acquire SA; thus, simply providing the same system and training will not ensure similar SA across different individuals. Endsley's model shows how SA "provides the primary basis for subsequent decision making and performance in the operation of complex, dynamic systems" (Endsley, 1995a, p. 65). Although alone it cannot guarantee successful decision making, SA does support the necessary input processes (e.g., cue recognition, situation assessment, prediction) upon which good decisions are based (Artman, 2000).
SA also involves both a temporal and a spatial component. Time is an important concept in SA, as SA is a dynamic construct, changing at a tempo dictated by the actions of individuals, task characteristics, and the surrounding environment. As new inputs enter the system, the individual incorporates them into this mental representation, making changes as necessary in plans and actions in order to achieve the desired goals. SA also involves spatial knowledge about the activities and events occurring in a specific location of interest to the individual. Thus, the concept of SA includes perception, comprehension, and projection of situational information, as well as temporal and spatial components.
In summary, the model consists of several key factors:
- Perception, comprehension, and projection as three levels of SA,
- The role of goals and goal directed processing in directing attention and interpreting the significance of perceived information,
- The role of information salience in "grabbing" attention in a data-driven fashion, and the importance of alternating goal-driven and data-driven processing,
- The role of expectations (fed by the current model of the situation and by long-term memory stores) in directing attention and interpreting information,
- The heavy demands on limited working memory restricting SA for novices and for those in novel situations, but the tremendous advantages of mental models and pattern matching to prototypical schema that largely circumvent these limits,
- The use of mental models for providing a means for integrating different bits of information and comprehending its meaning (relevant to goals) and for allowing people to make useful projections of likely future events and states,
- Pattern matching to schema—prototypical states of the mental model—that provides rapid retrieval of comprehension and projection relevant to the recognized situation and in many cases single-step retrieval of appropriate actions for the situation.
For a more complete description of the model, see Endsley (1995b) and Endsley (2004). See also Endsley (2000) for a review of other models of SA.
Criticism of modelEdit
Any model of situation awareness depends on cognitive and shared cognitive processes, and yet '...models of SA refer to cognitive processes in general terms, but do not specify exactly what processes are involved and to what extent.' (Banbury & Tremblay, 2004, p. xiii). This criticism is an example of the difficulty that cognitive science has in addressing a concept such as SA, which through its definition and assumptions appears to stand robustly, however when the theorized processes are exposed at the cognitive level of analysis assumptions must be radically reviewed. Researchers have regularly raised these concerns - notably in Flach (1995) and more recently in Banbury & Tremblay (2004). To date the most widely cited model of SA is lacking in support from cognitive science, one notable observation that still stands is that:
'The test of situation awareness as a construct will be in its ability to be operationalized in terms of objective, clearly specified independent (stimulus manipulation) and dependent (response difference) variables ... Otherwise, SA will be yet another buzzword to cloak scientists' ignorance.' (Flach, J., 1995, p. 155)
Another criticism of the model comes from a study done in 2015 which looked at situational awareness in tasks where relevant knowledge about the task could be found through other mediums, other than directly asking the collaborator. It was found that in these types of tasks, verbal communication lengthens the time it takes to complete a task when compared to people completing a task individually.
In team operationsEdit
In many systems and organizations, people work not just as individuals, but as members of a team. Thus, it is necessary to consider the SA of not just individual team members, but also the SA of the team as a whole. To begin to understand what is needed for SA within teams, it is first necessary to clearly define what constitutes a team. A team is not just any group of individuals; rather teams have a few defining characteristics. As defined by Salas et al. (1992), a team is:
"a distinguishable set of two or more people who interact dynamically, interdependently and adaptively toward a common and valued goal/objective/mission, who have each been assigned specific roles or functions to perform, and who have a limited life span of membership."
Team SA is defined as "the degree to which every team member possesses the SA required for his or her responsibilities" (Endsley, 1995b, p. 39; see also Endsley, 1989). The success or failure of a team depends on the success or failure of each of its team members. If any one of the team members has poor SA, it can lead to a critical error in performance that can undermine the success of the entire team. By this definition, each team member needs to have a high level of SA on those factors that are relevant for his or her job. It is not sufficient for one member of the team to be aware of critical information if the team member who needs that information is not aware.
In a team, each member has a subgoal pertinent to his/her specific role that feeds into the overall team goal. Associated with each member's subgoal are a set of SA elements about which he/she is concerned. Team SA, therefore, can be represented as shown in Figure 2. As the members of a team are essentially interdependent in meeting the overall team goal, some overlap between each member's subgoal and their SA requirements will be present. It is this subset of information that constitutes much of team coordination. That coordination may occur as a verbal exchange, a duplication of displayed information, or by some other means.
Shared situation awareness can be defined as "the degree to which team members possess the same SA on shared SA requirements" (Endsley & Jones, 1997, p. 47; 2001, p. 48). As implied by this definition, there are information requirements that are relevant to multiple team members. A major part of teamwork involves the area where these SA requirements overlap—the shared SA requirements that exist as a function of the essential interdependency of the team members. In a poorly functioning team, two or more members may have different assessments on these shared SA requirements and thus behave in an uncoordinated or even counter-productive fashion. Yet in a smoothly functioning team, each team member shares a common understanding of what is happening on those SA elements that are common—shared SA. Thus, shared SA refers to the overlap between the SA requirements of the team members, as presented in Figure 3. As depicted by the clear areas of the figure, not all information needs to be shared. Clearly, each team member is aware of much that is not pertinent to the others on the team. Sharing every detail of each person's job would only create a great deal of "noise" to sort through to get needed information. It is only that information which is relevant to the SA requirements of each team member that is needed.
Team SA modelEdit
The situation awareness of the team as a whole, therefore, is dependent upon both (1) a high level of SA among individual team members for the aspects of the situation necessary for their job; and (2) a high level of shared SA between team members, providing an accurate common operating picture of those aspects of the situation common to the needs of each member (Endsley & Jones, 2001). Endsley and Jones (1997; 2001) describe a model of team situation awareness as a means of conceptualizing how teams develop high levels of shared SA across members. Each of these four factors—requirements, devices, mechanisms and processes—act to help build team and shared SA.
1. Team SA requirements – the degree to which the team members know which information needs to be shared, including their higher level assessments and projections (which are usually not otherwise available to fellow team members), and information on team members' task status and current capabilities.
2. Team SA devices – the devices available for sharing this information, which can include direct communication (both verbal and non-verbal), shared displays (e.g., visual or audio displays, or tactile devices), or a shared environment. As non-verbal communication, such as gestures and display of local artifacts, and a shared environment are usually not available in distributed teams, this places far more emphasis on verbal communication and communication technologies for creating shared information displays.
3. Team SA mechanisms – the degree to which team members possess mechanisms, such as shared mental models, which support their ability to interpret information in the same way and make accurate projections regarding each other's actions. The possession of shared mental models can greatly facilitate communication and coordination in team settings.
4. Team SA processes – the degree to which team members engage in effective processes for sharing SA information which may include a group norm of questioning assumptions, checking each other for conflicting information or perceptions, setting up coordination and prioritization of tasks, and establishing contingency planning among others.
In time critical decision-making processesEdit
There are many industries where it is critical to make a correct decision within a strict time limit, based on the decision-maker's knowledge of the current situation: for example air traffic controllers or medical providers (e.g. anesthesiologists). In these situations it is common that the key decision maker is supported by other team members or by complex monitoring systems feeding them information, which can involve multiple sources and formats of information. Even in these time-critical situations, the importance of having situation awareness (SA) is not constant: i.e. it is more critical to the outcome to have better SA for non-standard situations, such as points of high information traffic, extraneous activity and unforeseeable events. These ‘points of fracture’ are likely to cause additional workload on the individuals and therefore affect their SA and the time to make the decision. At the critical point the perceived situational awareness utilized to make the decision is directly affected by the cognitive workload to gain, comprehend and process the SA that is coming in to the operator, both general background SA and the SA specifically related to the decision. (Smith, K. T. 2013) In other words, if everything is going OK the level of SA you have is not as critical as it is when something unusual happens or something goes wrong.
Research into the decision making process is an increasing area of interest and the identification of this type of relationship has led to the development of at least one integrated conceptual framework (developed by K Tara Smith) that attempts to accommodate all of the impactors on the decision-making process, defining how they impact on the individual's ability to acquire their SA. This involves aligning the terms and concepts used by different research areas, so that the causal relationships can be identified and defined.
This approach of integrating situation awareness, workload, signal processing theory, decision theory, etc. tends to subtly change the questions that are asked during the analysis process from quantifying and qualifying the SA to measures of the probabilistic aspects of a decision, such as the number of interrelationships, the certainty and time-lag of the information arriving, risk to desired outcome or effect, etc., together with the processing aspects, to do with the number of signals, accuracy and completeness of the information and importance to the operational context. In other words, instead of asking does a modification to the system provide more SA, we are asking does this modification to the system provide more SA in a form that can be used at the time when it is needed?
While the SA construct has been widely researched, the multivariate nature of SA poses a considerable challenge to its quantification and measurement (for a detailed discussion on SA measurement, see Endsley & Garland, 2000; Fracker, 1991a; 1991b). In general, techniques vary in terms of direct measurement of SA (e.g., objective real-time probes or subjective questionnaires assessing perceived SA) or methods that infer SA based on operator behavior or performance. Direct measures are typically considered to be "product-oriented" in that these techniques assess an SA outcome; inferred measures are considered to be "process-oriented," focusing on the underlying processes or mechanisms required to achieve SA (Graham & Matthews, 2000). These SA measurement approaches are further described next.
Objective measures directly assess SA by comparing an individual's perceptions of the situation or environment to some "ground truth" reality. Specifically, objective measures collect data from the individual on his or her perceptions of the situation and compare them to what is actually happening to score the accuracy of their SA at a given moment in time. Thus, this type of assessment provides a direct measure of SA and does not require operators or observers to make judgments about situational knowledge on the basis of incomplete information. Objective measures can be gathered in one of three ways: real-time as the task is completed (e.g., "real-time probes" presented as open questions embedded as verbal communications during the task – Jones & Endsley, 2000), during an interruption in task performance (e.g., situation awareness global assessment technique (SAGAT) – Endsley, 1995a, or the WOMBAT situational awareness and stress tolerance test mostly used in aviation since the late 1980s and often called HUPEX in Europe), or post-test following completion of the task.
Subjective measures directly assess SA by asking individuals to rate their own or the observed SA of individuals on an anchored scale (e.g., participant situation awareness questionnaire (PSAQ) – Strater, Endsley, Pleban, & Matthews, 2001; the situation awareness rating technique (SART) – Taylor, 1989). Subjective measures of SA are attractive in that they are relatively straightforward and easy to administer. However, several limitations should be noted. Individuals making subjective assessments of their own SA are often unaware of information they do not know (the "unknown unknowns"). Subjective measures also tend to be global in nature, and, as such, do not fully exploit the multivariate nature of SA to provide the detailed diagnostics available with objective measures. Nevertheless, self-ratings may be useful in that they can provide an assessment of operators' degree of confidence in their SA and their own performance. Measuring how SA is perceived by the operator may provide information as important as the operator's actual SA, since errors in perceived SA quality (over-confidence or under-confidence in SA) may have just as harmful an effect on an individual's or team's decision-making as errors in their actual SA (Endsley, 1998).
Subjective estimates of an individual's SA may also be made by experienced observers (e.g., peers, commanders, or trained external experts). These observer ratings may be somewhat superior to self-ratings of SA because more information about the true state of the environment is usually available to the observer than to the operator, who may be focused on performing the task (i.e., trained observers may have more complete knowledge of the situation). However, observers have only limited knowledge about the operator's concept of the situation and cannot have complete insight into the mental state of the individual being evaluated. Thus, observers are forced to rely more on operators' observable actions and verbalizations in order to infer their level of SA. In this case, such actions and verbalizations are best assessed using performance and behavioral measures of SA, as described next.
Performance and behavioral measuresEdit
Performance measures "infer" SA from the end result (i.e., task performance outcomes), based on the assumption that better performance indicates better SA. Common performance metrics include quantity of output or productivity level, time to perform the task or respond to an event, and the accuracy of the response or, conversely, the number of errors committed. The main advantage of performance measures is that these can be collected objectively and without disrupting task performance. However, although evidence exists to suggest a positive relation between SA and performance, this connection is probabilistic and not always direct and unequivocal (Endsley, 1995b). In other words, good SA does not always lead to good performance and poor SA does not always lead to poor performance (Endsley, 1990). Thus, performance measures should be used in conjunction with others measures of SA that directly assess this construct.
Behavioral measures also "infer" SA from the actions that individuals choose to take, based on the assumption that good actions will follow from good SA and vice versa. Behavioral measures rely primarily on observer ratings, and are, thus, somewhat subjective in nature. To address this limitation, observers can be asked to evaluate the degree to which individuals are carrying out actions and exhibiting behaviors that would be expected to promote the achievement of higher levels of SA (see, for example, the situation awareness behaviorally anchored rating scale (SABARS) – Matthews, Pleban, Endsley, & Strater, 2000; Strater et al., 2001). This approach removes some of the subjectivity associated with making judgments about an individual's internal state of knowledge by allowing them to make judgments about SA indicators that are more readily observable.
Process indices examine how individuals process information in their environment, such as by analyzing communication patterns between team members or using eye tracking devices. Team communication (particularly verbal communication) supports the knowledge building and information processing that leads to SA construction (Endsley & Jones, 1997). Thus, since SA may be distributed via communication, computational linguistics and machine learning techniques can be combined with natural language analytical techniques (e.g., Latent Semantic Analysis) to create models that draw on the verbal expressions of the team to predict SA and task performance (Bolstad, Cuevas, Gonzalez, & Schneider, 2005; Bolstad, Foltz, Franzke, Cuevas, Rosenstein, & Costello, 2007). Although evidence exists to support the utility of communication analysis for predicting team SA (Foltz, Bolstad, Cuevas, Franzke, Rosenstein, & Costello, in press), time constraints and technological limitations (e.g., cost and availability of speech recording systems and speech-to-text translation software) may make this approach less practical and viable in time-pressured, fast-paced operations.
Psycho-physiological measures also serve as process indices of operator SA by providing an assessment of the relationship between human performance and a corrected change in the operator's physiology (e.g., French, Clark, Pomeroy, Seymour, & Clarke, 2007). In other words, cognitive activity is associated with changes in the operator's physiological states. For example, the operator's overall functional state (as assessed using psycho-physiological measures, such as electroencephalographic (EEG) data, eyeblinks, and cardiac activity) may provide an indication as to whether the operator is sleep fatigued at one end of the continuum, or mentally overloaded at the other end (Wilson, 2000). Other psycho-physiological measures, such as event related potentials (ERP), event related desynchronization (ERD), transient heart rate (HR), and electrodermal activity (EDA), may be useful for evaluating an operator's perception of critical environmental cues, that is, to determine if the operator has detected and perceived a task-relevant stimulus (Wilson, 2000). In addition, it is also possible to use psycho-physiological measures to monitor operators' environmental expectancies, that is, their physiological responses to upcoming events, as a measure of their current level of SA (Wilson, 2000).
Multi-faceted approach to measurementEdit
The multivariate nature of SA significantly complicates its quantification and measurement, as it is conceivable that a metric may only tap into one aspect of the operator's SA. Further, studies have shown that different types of SA measures do not always correlate strongly with each other (cf. Durso, Truitt, Hackworth, Crutchfield, Nikolic, Moertl, Ohrt, & Manning, 1995; Endsley, Selcon, Hardiman, & Croft, 1998; Vidulich, 2000). Accordingly, rather than rely on a single approach or metric, valid and reliable measurement of SA should utilize a battery of distinct yet related measures that complement each other (e.g., Harwood, Barnett, & Wickens, 1988). Such a multi-faced approach to SA measurement capitalizes on the strengths of each measure while minimizing the limitations inherent in each.
Following Endsley's paradigm and with cognitive resource management model with neurofeedback techniques, Spanish Pedagogist María Gabriela López García (2010) implemented and developed a new SA training pattern. The first organization to implement this new pattern design by López García is the SPAF (Spanish Air Force). She has trained EF-18 fighter pilots and Canadair firefighters.
This situation awareness training aims to avoid losing SA and provide pilots cognitive resources to always operate below the maximum workload that they can withstand. This provides not only a lower probability of incidents and accidents by human factors, but the hours of operation are at their optimum efficiency, extending the operating life of systems and operators.
Emergency medical call-outsEdit
In first aid medical training provided by the American Red Cross, the need to be aware of the situation within the area of influence as one approaches an individual requiring medical assistance is the first aspect for responders to consider Examining the area and being aware of potential hazards, including the hazards which may have caused the injuries being treated, is an effort to ensure that responders do not themselves get injured and require treatment as well.
Situation awareness for first responders in medical situations also includes evaluating and understanding what happened to avoid injury of responders and also to provide information to other rescue agencies which may need to know what the situation is via radio prior to their arrival on the scene.
In a medical context, situation awareness is applied to avoid further injury to already-injured individuals, to avoid injury to medical responders, and to inform other potential responders of hazardous conditions prior to their arrival.
In transport safety, a very important aspect is situational awareness. A loss in situational awareness has led to many plane crashes and numerous train disasters, including the 2015 Philadelphia train derailment as an example.
Search and rescueEdit
Within the search and rescue context, situational awareness is applied primarily to avoid injury to search crews however being aware of the environment, the lay of the land, and the many other factors of influence within one's surroundings assists in the location of injured or missing individuals Public safety agencies are increasingly using situational awareness applications like ATAK on mobile devices to improve situational awareness.
Forestry crosscut saw / chainsawEdit
In the United States Forest Service the use of chainsaws and crosscut saws requires training and certification. A great deal of that training describes situational awareness as an approach toward environmental awareness but also self-awareness which includes being aware of one's own emotional attitude, tiredness, and even caloric intake.
Situational awareness in the forest context also includes evaluating the environment and the potential safety hazards within a saw crew's area of influence. As a sawyer approaches a task, the ground, wind, cloud cover, hillsides, and many other factors are examined and are considered proactively as part of trained sawyers' ingrained training.
Dead or diseased trees within the reach of saw team crews are evaluated, the strength and direction of the wind is evaluated. The lay of tree sections to be bucked or the lean of a tree to be felled is evaluated within the context of being aware of where the tree will fall or move to when cut, where the other members of the saw team are located, how they are moving, whether hikers are within the area of influence, whether hikers are moving or are stationary.
Law enforcement training includes being situationally aware of what is going on around the police officer before, during, and after interactions with the general public while also being fully aware of what is happening around the officer in areas not currently the focus of an officer's immediate task.
In the law enforcement context, situation awareness involves keeping eyes and ears open and evaluating what is happening on the way to a specific task, remaining fully aware of one's surroundings during the commission of a task, and retaining one's awareness when exiting a specific task. Situational awareness in a law enforcement setting seeks to ensure that a police officer does not become focused and engrossed on a specific task or problem to the exclusion of being aware of approaching potential hazards or to the exclusion of being aware of other tasks of higher priority. Situational awareness is a 24/7 process and policy laid down in officer training.
Cybersecurity threat operationsEdit
In cybersecurity, consider situational awareness, for threat operations, is being able to perceive threat activity and vulnerability in context so that the following can be actively defended: data, information, knowledge, and wisdom from compromise. Situational awareness is achieved by developing and using solutions that often consume data and information from many different sources. Technology and algorithms are then used to apply knowledge and wisdom in order to discern patterns of behavior that point to possible, probable, and real threats.
Situational awareness for cybersecurity threat operations teams appears in the form of a condensed, enriched, often graphical, prioritized, and easily searchable view of systems that are inside or related to security areas of responsibility (such as corporate networks or those used for national security interests). Different studies have analyzed the perception of security and privacy in the context of eHealth, network security, or using collaborative approaches to improve the awareness of users. There are also research efforts to automate the processing of communication network information in order to obtain or improve cyber-situational awareness.
Situation awareness-based agency transparency (SAT) modelEdit
As the capabilities of technological agents increases, it becomes more important that their actions and underlying rational becomes transparent. In the military realm, agent transparency has been investigated as unmanned vehicles are being employed more frequently. In 2014, researchers at the U.S. Army Research Laboratory reported the Situation Awareness-based Agent Transparency (SAT), a model designed to increase transparency through user interface design. When it comes to automation, six barriers that have been determined to discourage human trust in: “‘low observability, predictability, directability and auditability’...[and] ‘low mutual understanding of common goals.’” The researchers at the U.S. Army Research Laboratory designed three levels of situational awareness transparency based on Endsley's theory of prediction, comprehension, and projection. The greater the level of situational awareness, they claimed, the more information the agent conveys to the user.
A 2018 publication from the U.S. Army Research Laboratory evaluated how varying transparency levels in the SAT affects the operator workload and a human's understanding of when it is necessary to intervene in the agent's decision making. The researchers refer to this supervisory judgement as calibration. The group split their SAT model research into two efforts: the Intelligent Agent Transparency in Human Agent Transparency for Multi UxV Management (IMPACT) and the Autonomous Squad Member (ASM) projects.
SAT for intelligent agent transparency in human agent transparency for multi UxV managementEdit
Scientists provided three standard levels of SAT in addition to a fourth level which included the agent's level of uncertainty in its decision in unmanned vehicles. The stated goal of this research was to determine how modifying levels of SAT affected user performance, situation awareness, and confidence in the agent. The scientists stated that their experimental results support that increased agent transparency improved the performance of the operator and human confidence on the agent without a significant effect on the workload. When the agent communicated levels of uncertainty in the task assigned, those involved in the experimentation displayed more trust in the agent.
SAT in an autonomous squad memberEdit
The ASM research was conducted by providing a simulation game in which the participant had to complete a training course with an ASM, a ground robot that communicates with infantry. The participants had to multitask, evaluating potential threats while monitoring the ASM's communications on the interface. According to that research, experimental results demonstrated that the greatest confidence calibration occurred when the agent communicated information of all three levels of SAT. The group of scientists from the U.S. Army Research Laboratory developed transparency visualization concepts in which the agents can communicate their plans, motivations, and projected outcomes through icons. The agent has been reported to be able to relate its resource usage, reasoning, predicted resource loss, progress towards task completion, etc. Unlike in the IMPACT research, the agent informing the user of its level of uncertainty in decision making, no increase in trust was observed.
Methods of gaining situational awarenessEdit
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Crowdsourcing, made possible by the rise of social media and ubiquitous mobile access has a potential for considerably enhancing situation awareness of both, responsible authorities and citizens themselves for emergency and crisis situations by employing or using "citizens as sensors". For instance, analysis of content posted on online social media like Facebook and Twitter using data mining, machine learning and natural language processing techniques may provide situational information. A crowdsourcing approach to sensing, particularly in crisis situations, has been referred to as 'crowdsensing'. Crowdmapping is a subtype of crowdsourcing by which aggregation of crowd-generated inputs such as captured communications and social media feeds are combined with geographic data to create a digital map that is as up-to-date as possible that can improve situational awareness during an incident and be used to support incident response.
Since 2012 the National Information Sharing Consortium (NISC) has worked to provide "the right information to the right people at the right time" by use of common terminology among the emergency management community and first-responders with a mission of standardizing the structured geo-spatial data to be shared online over a variety of platforms. The result is to create a Common Operating Picture (COP) that generates accurate and timely information displayed visually, both at the strategic level for decision makers and at the tactical level for people on site. The NISC promotes the sharing of code, data widgets, and training in order to increase the quality of the situational awareness. Large scale exercises like the Capstone-14 week-long event coordinated by the Central United States Earthquake Consortium (CUSEC) and actual operational use of these data sharing methods have also advanced the work of NISC to expand the usability of GIS-based information sharing for enhanced situational awareness.
Military training methodsEdit
There are two training scenarios designed to increase the situational awareness skills of military professionals, and first responders in police and emergency services. The first, Kim's Game, has a more common place in the Marine Corps sniper school and police academies. The name is derived from the novel Kim which references the game to a spy school lesson. The game involves a tray with various items such as spoons, pencils, bullets, and any other items the soldiers would be familiar with. The participants are given one minute to view all of these items before they are covered up with a blanket. The participants would then individually list the items that they saw, the one with the most correct answers would win the game. The same game is played in young scouting and girl guide groups as well to teach children quick memorisation skills.
The second method is a more practical military application of Kim's Game. It starts with a field area (jungle, bush or forest) of about five meters wide to 10 meters deep where various items, some camouflaged and some not, to be located in the area on the ground and in the trees at eyesight level. Again, these items would be ones that are familiar to the soldiers undergoing the exercise. The participants would be given 10 minutes to view the area from one place and take a mental note of the items they saw. Once their 10 minutes is up, the soldier would then be required to do a repetition of certain exercises such as burpees, designed to simulate the stress of a physically demanding environment. Once the participant completes the exercise, they would list the items they saw. The points would be tallied in the end to find the winner.
- Alert state
- Aviation safety – A state in which risks associated with aviation are at an acceptable level
- Brownout (aeronautics)
- Decision-making – Cognitive process resulting in choosing a course of action
- Diving safety – Safety of underwater diving activities
- Information integration
- Korean Air Lines Flight 007 – 1983 shoot-down of a civilian airliner
- Situated cognition
- Situation room
- Spatial disorientation – Inability of a person to correctly determine their body position in space
- Single-pilot resource management
- Endsley, 1995b, p. 36
- Nullmeyer, Stella, Montijo, & Harden 2005
- Schulz CM et al.Situation Awareness in Anesthesia - Concept and Research, Anesthesiology 2013.
- Blandford & Wong 2004; Gorman, Cooke, & Winner 2006
- Flin & O'Connor, 2001
- Hartel, Smith, & Prince, 1991; Merket, Bergondy, & Cuevas-Mesa, 1997; Nullmeyer, Stella, Montijo, & Harden, 2005
- Endsley, Micah; Jones, Debra (2016-04-19). Designing for Situation Awareness (Second ed.). CRC Press. p. 13. ISBN 978-1-4200-6358-5.
- Lundberg, 2015
- Press, 1986
- Biferno, M.A. "Flight Crew Computer Interaction", Douglas Aircraft Company, Internal Research and Development. Long Beach, CA.
- Biferno, M.A., "Mental Workload Measurement", Douglas Aircraft Company, Internal Research and Development, Long Beach, CA.
- Dawson, M.E., Biferno, M.A. (1973). "Concurrent measurement of awareness and electrodermal classical conditioning", Journal of Experimental Psychology', 101, 55-62.
- Biferno, M.A.; Dawson, M.E. (1977). "The onset of contingency awareness and electrodermal classical conditioning: An analysis of temporal relationships during acquisition and extinction". Psychophysiology. 14 (2): 164–171. doi:10.1111/j.1469-8986.1977.tb03370.x.
- Biferno, M.A. (1985). "Relationship between event-related potential components and ratings of workload and fatigue", NASA-Ames, Moffett Field, CA, NASA contract report 177354.
- Biferno, M. A. & Stanley, D. L. (1983). The Touch-Sensitive Control/Display Unit: A promising Computer Interface. Technical Paper 831532, Aerospace Congress & Exposition, Long Beach, CA: Society of Automotive Engineers.
- Watts, 2004
- Spick, 1988
- Endsley, 1995b
- Gaba, Howard & Small, 1995
- Collier & Follesf, 1995
- Bolstad, 2000
- Sollenberger & Stein, 1995
- S.M. Fiore, personal communication, November 6, 2007
- Blasch, E., Bosse, E., and Lambert, D. A., High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA, 2012.
- Boddhu, Sanjay K., et al. (2012). "Increasing situational awareness using smartphones." SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2012.
- Sanjay Kumar Boddhu, Matt McCartney, Oliver Ceccopieri, et al., "A collaborative smartphone sensing platform for detecting and tracking hostile drones", Proceedings of SPIE Vol. 8742, 874211 (2013)
- Dostal, 2007
- Endsley, 2000
- Endsley & Jones, 1997
- Sarter & Woods, 1991
- Glaser, 1989
- Kozlowski, 1998
- Endsley, 1997
- Serfaty, MacMillan, Entin, & Entin, 1997
- Klein, Moon, and Hoffman, 2006
- Klein, Moon, and Hoffman, 2006, p. 71
- Endsley, 2004
- McAnally, K.; Davey, C.; White, D.; Stimson, M.; Mascaro, S.; Korb, K (2018). "Inference in the wild: A framework for human situation assessment and a case study of air combat". Cognitive Science. 42 (7): 2181–2204. doi:10.1111/cogs.12636.
- Sloman, S. (2005). Causal models: How people think about the world and its alternatives. Oxford University Press. ISBN 9780195183115. Retrieved 2 May 2019.
- Kozlov, Michail; Engelmann, Tanja; Buder, Jurgen; Hesse, Friedrich W. (October 2015). "Is knowledge best shared or given to individuals? Expanding the Content-based Knowledge Awareness paradigm". Computers in Human Behavior. 51: 15–23. doi:10.1016/j.chb.2015.04.029.
- Smith, K. T. (2013) Building a human capability decision engine. Contemporary Ergonomics and Human Factors 2013 Proceedings of the international conference on Ergonomics & Human Factors 2013, 395–402 http://www.crcnetbase.com/doi/abs/10.1201/b13826-84
- Simmon, D.A. (1998). Boeing 757 CFIT Accident at Cali, Colombia, becomes focus of lessons learned. Flight Safety Digest, 17, 1-31.
- Revista Aviador --Official Spanish Commercial Pilots Association magazine--, July–August 2011, # 61, 38-39 pag.
- Revista de Aeronáutica y Astronáutica --Official SPAF magazine-- May 2012 issue, 436-439 pag.
- Cognitive Systems Engineering Jens Rasmussen and others.
- First Aid, Protect Yourself, American red Cross – Accessed 01/Aug/13
- First Aid, Understanding What Happened – Accessed 01/Aug/13
- Train Engineer’s Loss of Situational Awareness Led to Amtrak Derailment; NTSB Says Technology Could Have Prevented Fatal Accident
- Mountain Rescue Association Blog, Situational Awareness in Mountain Rescue Operations – Accessed 01/Aug/13
- US Forest Service, Chain Saw and Crosscut Saw Training Course – Accessed 01/Aug/13
- – U.S. Forest Service, Chapter 2, Page 7, Situational Awareness (PDF) – Accessed 01/Aug/13
- Police Chief, Improving Situational Awareness Archived 2014-01-08 at the Wayback Machine – Accessed 01/Aug/13
- – Law Enforcement Today, Police Situational Awareness Archived 2013-08-10 at the Wayback Machine – Accessed 01/Aug/13
- Personal Safety, What Is Situational Awareness? – Accessed 01/Aug/13
- Bellekens, Xavier; Hamilton, Andrew; Seeam, Preetila; Nieradzinska, Kamila; Franssen, Quentin; Seeam, Amar (2016). Pervasive eHealth services a security and privacy risk awareness survey. 2016 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). pp. 1–4. doi:10.1109/CyberSA.2016.7503293. ISBN 978-1-5090-0703-5.
- Best, Daniel M.; Bohn, Shawn; Love, Douglas; Wynne, Adam; Pike, William A. (2010). Real-time visualization of network behaviors for situational awareness. Proceedings of the Seventh International Symposium on Visualization for Cyber Security - VizSec '10. pp. 79–90. doi:10.1145/1850795.1850805. ISBN 9781450300131.
- Mathews, Mary; Halvorsen, Paul; Joshi, Anupam; Finin, Tim (2012). A Collaborative Approach to Situational Awareness for CyberSecurity. Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. doi:10.4108/icst.collaboratecom.2012.250794. ISBN 978-1-936968-36-7.
- Sikos, Leslie; Stumptner, Markus; Mayer, Wolfgang; Howard, Catherine; Voigt, Shaun; Philp, Dean (2018), Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness, Lecture Notes in Computer Science, 11062, Cham: Springer, p. 132–143, doi:10.1007/978-3-319-99247-1_12, ISBN 978-3-319-99246-4
- "Army scientists improve human-agent teaming by making AI agents more transparent | U.S. Army Research Laboratory". arl.army.mil. Retrieved 2018-08-15.
- Boyce, Michael; Chen, Joyce; Selkowitz, Andrew; Lakhmani, Shan (May 2015). "Agent Transparency for an Autonomous Squad Member" (PDF). Retrieved 7/28/18. Check date values in:
- Chen, Jessie Y. C.; Lakhmani, Shan G.; Stowers, Kimberly; Selkowitz, Anthony R.; Wright, Julia L.; Barnes, Michael (2018-02-23). "Situation awareness-based agent transparency and human-autonomy teaming effectiveness". Theoretical Issues in Ergonomics Science. 19 (3): 259–282. doi:10.1080/1463922x.2017.1315750. ISSN 1463-922X.
- "CrowdSA - Crowdsourced Situation Awareness for Crisis Management". cis.jku.at. Retrieved 9 January 2017.
- "SITUATION AWARENESS AND RELIEF SYSTEM DURING DISASTER EVENTS" (PDF). International Journal of Research in Science & Engineering. Retrieved 9 January 2017.
- "Crowdsourcing public safety: Building community resilience by enhancing citizen situation awareness capability | RISE:2017". RISE:2017, Northeastern University. Retrieved 9 January 2017.
- Shepard, Steven (2014-07-06). Telecommunications Crash Course, Third Edition. McGraw Hill Professional. ISBN 9780071797115. Retrieved 9 January 2017.
- Poblet, Marta; García-Cuesta, Esteban; Casanovas, Pompeu (2014). Crowdsourcing Tools for Disaster Management: A Review of Platforms and Methods (PDF). Lecture Notes in Computer Science. 8929. pp. 261–274. doi:10.1007/978-3-662-45960-7_19. ISBN 978-3-662-45959-1. ISSN 0302-9743. Retrieved 9 January 2017.
- "Crowdsourcing Information for Enhanced Disaster Situation Awareness and Emergency Preparedness and Response" (PDF). Retrieved 9 January 2017. Cite journal requires
- Arkian, Hamid Reza; Diyanat, Abolfazl; Pourkhalili, Atefe (2017). "MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications". Journal of Network and Computer Applications. 82: 152–165. Bibcode:2017JNCA...93...27H. doi:10.1016/j.jnca.2017.01.012.
- Basu, Moumita; Bandyopadhyay, Somprakash; Ghosh, Saptarshi (2016). "Post Disaster Situation Awareness and Decision Support Through Interactive Crowdsourcing". Procedia Engineering. 159: 167–173. Bibcode:2017ProEn.204...69B. doi:10.1016/j.proeng.2016.08.151.
- Basu, Moumita; Bandyopadhyay, Somprakash; Ghosh, Saptarshi (2016). "Post Disaster Situation Awareness and Decision Support Through Interactive Crowdsourcing" (PDF). Procedia Engineering. 159: 167–173. Bibcode:2017ProEn.204...69B. doi:10.1016/j.proeng.2016.08.151. Retrieved 9 January 2017.
- Haddawy, Peter; Frommberger, Lutz; Kauppinen, Tomi; De Felice, Giorgio; Charkratpahu, Prae; Saengpao, Sirawaratt; Kanchanakitsakul, Phanumas (1 January 2015). Situation Awareness in Crowdsensing for Disease Surveillance in Crisis Situations (PDF). Proceedings of the Seventh International Conference on Information and Communication Technologies and Development. pp. 38:1–38:5. doi:10.1145/2737856.2737879. ISBN 9781450331630. Retrieved 9 January 2017.
- Aitamurto, Tanja (8 May 2015). "Crowdsourcing as a Knowledge-Search Method in Digital Journalism". Digital Journalism. 4 (2): 280–297. doi:10.1080/21670811.2015.1034807. ISSN 2167-0811. Retrieved 6 January 2017.
- Aitamurto, Tanja (1 October 2015). "Motivation Factors in Crowdsourced Journalism: Social Impact, Social Change, and Peer Learning". Retrieved 6 January 2017.
- Sutter, John D. "Ushahidi: How to 'crowdmap' a disaster". CNN. Retrieved 6 January 2017.
- The Impact of Crowdsourcing on Organisational Practices: The Case of Crowdmapping. ISBN 978-3-00-050284-2. Retrieved 6 January 2017.
- "Crowdsourced counter-surveillance: Examining the subversion of random breath testing stations by social media facilitated crowdsourcing" (PDF). Retrieved 6 January 2017.
- "Concepts to Know: Crowdmapping". Kimo Quaintance. 4 September 2011. Retrieved 6 January 2017.
- "Chemical Hazards and Poisons Report" (PDF). Public Health England. Retrieved 6 January 2017.
- "Situational Awareness: How You Can Master Survival, Work and Life". The Prepping Guide. 2017-09-13. Retrieved 2017-12-13.
- FDSE's COP Page 
- Adam, E.C. (1993). Fighter cockpits of the future. Proceedings of 12th IEEE/AIAA Digital Avionics Systems Conference (DASC), 318–323.
- Artman, H. (2000). Team situation assessment and information distribution. Ergonomics, 43(8), 1111–1128.
- Banbury, S. & Tremblay, S. (2004). A cognitive approach to situation awareness: Theory and application (pp. 317–341). Aldershot, UK: Ashgate Publishing.
- Blandford, A. & Wong, W. (2004). Situation awareness in emergency medical dispatch. International Journal of Human–Computer Studies, 61, 421–452.
- Bolstad, C.A. (2000). Age-related factors affecting the perception of essential information during risky driving situations. Paper presented at the Human Performance Situation Awareness and Automation: User-Centered Design for the New Millennium Conference, Savannah, GA.
- Bolstad, C.A., Cuevas H.M., Gonzalez, C., & Schneider, M. (2005). Modeling shared situation awareness. Proceedings of the 14th Conference on Behavior Representation in Modeling and Simulation (BRIMS). Los Angeles, CA.
- Bolstad, C.A., Foltz, P., Franzke, M., Cuevas, H.M., Rosenstein, M., & Costello, A.M. (2007). Predicting situation awareness from team communications. Proceedings of the 51st Annual Meeting of the Human Factors and Ergonomics Society. Santa Monica, CA: HFES.
- Collier, S.G. & Follesf, K. (1995). SACRI: A measure of situation awareness for nuclear power plant control rooms. Proceedings of an International Conference: Experimental Analysis and Measurement of Situation Awareness (pp. 115–122). Daytona Beach, FL.
- Dominguez, C., Vidulich, M., Vogel, E. & McMillan, G. (1994). Situation awareness: Papers and annotated bibliography. Armstrong Laboratory, Human System Center, ref. AL/CF-TR-1994-0085.
- Dostal, B.C. (2007). Enhancing situational understanding through the employment of unmanned aerial vehicles. Army Transformation Taking Shape ...Interim Brigade Combat Team Newsletter, No. 01–18. Retrieved November 7, 2007, from http://www.globalsecurity.org/military/library/report/call/call_01-18_ch6.htm
- Durso, F.T., Truitt, T.R., Hackworth, C.A., Crutchfield, J.M., Nikolic, D., Moertl, P.M., Ohrt, D., & Manning, C.A. (1995). Expertise and chess: A pilot study comparing situation awareness methodologies. In D.J. Garland & M.R. Endsley (Eds.), Experimental analysis and measurement of situation awareness (pp. 295–303). Daytona Beach, FL: Embry-Riddle Aeronautical University Press.
- Endsley, M.R. (1988). Situation awareness global assessment technique (SAGAT). Proceedings of the National Aerospace and Electronics Conference (NAECON), 789–795. New York: IEEE. doi:10.1109/NAECON.1988.195097
- Endsley, M.R. (1989). Final report: Situation awareness in an advanced strategic mission (No. NOR DOC 89-32). Hawthorne, CA: Northrop Corporation.
- Endsley, M.R. (1990). Predictive utility of an objective measure of situation awareness. Proceedings of the Human Factors Society 34th Annual Meeting (pp. 41–45). Santa Monica, CA: Human Factors Society.
- Endsley, M.R. (1995a). "Measurement of situation awareness in dynamic systems". Human Factors. 37 (1): 65–84. doi:10.1518/001872095779049499.
- Endsley, M.R. (1995b). "Toward a theory of situation awareness in dynamic systems". Human Factors. 37 (1): 32–64. doi:10.1518/001872095779049543.
- Endsley, M.R. (1997). The role of situation awareness in naturalistic decision making. In Zsambok, C.E. & G. Klein (Eds.), Naturalistic decision making (pp. 269–283). Mahwah, NJ: LEA.
- Endsley, M.R. (1998). A comparative analysis of SAGAT and SART for evaluations of situation awareness. In Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting (pp. 82–86). Santa Monica, CA: The Human Factors and Ergonomics Society.
- Endsley, M.R. (2000). Theoretical underpinnings of situation awareness: A critical review. In M.R. Endsley & D.J. Garland (Eds.), Situation awareness analysis and measurement. Mahwah, NJ: LEA.
- Endsley, M.R. (2004). Situation awareness: Progress and directions. In S. Banbury & S. Tremblay (Eds.), A cognitive approach to situation awareness: Theory and application (pp. 317–341). Aldershot, UK: Ashgate Publishing.
- Endsley, M.R. & Garland, D.J. (Eds.) (2000). Situation awareness analysis and measurement. Mahwah, NJ: Lawrence Erlbaum Associates.
- Endsley, M.R. & Jones, W.M. (1997). Situation awareness, information dominance, and information warfare (No. AL/CF-TR-1997-0156). Wright-Patterson AFB, OH: United States Air Force Armstrong Laboratory.
- Endsley, M.R. & Jones, W.M. (2001). A model of inter- and intrateam situation awareness: Implications for design, training and measurement. In M. McNeese, E. Salas & M. Endsley (Eds.), New trends in cooperative activities: Understanding system dynamics in complex environments. Santa Monica, CA: Human Factors and Ergonomics Society.
- Endsley, M.R., Selcon, S.J., Hardiman, T.D., & Croft, D.G. (1998). A comparative evaluation of SAGAT and SART for evaluations of situation awareness. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (pp. 82–86). Santa Monica, CA: Human Factors and Ergonomics Society. https://web.archive.org/web/20070928120127/http://www.satechnologies.com/Papers/pdf/HFES98-SAGATvSART.pdf
- Flach, J.M. (1995). "Situation awareness: Proceed with caution". Human Factors. 37 (1): 149–157. doi:10.1518/001872095779049480.
- Flin, R. & O'Connor, P. (2001). Applying crew resource management in offshore oil platforms. In E. Salas, C.A. Bowers, & E. Edens (Eds.), Improving teamwork in organization: Applications of resource management training (pp. 217–233). Hillsdale, NJ: Erlbaum.
- Foltz, P.W., Bolstad, C.A., Cuevas, H.M., Franzke, M., Rosenstein, M., & Costello, A.M. (in press). Measuring situation awareness through automated communication analysis. To appear in M. Letsky, N. Warner, S.M. Fiore, & C. Smith (Eds.), Macrocognition in teams. Aldershot, England: Ashgate.
- Fracker, M.L. (1991a). Measures of situation awareness: An experimental evaluation (Report No. AL-TR-1991-0127). Wright-Patterson Air Force Base, OH: Armstrong Laboratories.
- Fracker, M.L. (1991b). Measures of situation awareness: Review and future directions (Report No. AL-TR-1991-0128). Wright-Patterson Air Force Base, OH: Armstrong Laboratories.
- French, H.T., Clark, E., Pomeroy, D. Seymour, M., & Clarke, C.R. (2007). Psycho-physiological Measures of Situation Awareness. In M. Cook, J. Noyes & Y. Masakowski (eds.), Decision Making in Complex Environments. London: Ashgate. ISBN 0-7546-4950-4.
- Gaba, D.M.; Howard, S.K.; Small, S.D. (1995). "Situation awareness in anesthesiology". Human Factors. 37 (1): 20–31. doi:10.1518/001872095779049435. PMID 7790008.
- Glaser, R. (1989). Expertise and learning: How do we think about instructional processes now that we have discovered knowledge structures? In D. Klahr & K. Kotovsky (Eds.), Complex information processing: The impact of Herbert A. Simon (pp. 269–282). Hillsdale, NJ: LEA.
- Gorman, J.C. Cooke; Winner, J.L. (2006). "Measuring team situation awareness in decentralized command and control environments". Ergonomics. 49 (12–13): 1312–1325. doi:10.1080/00140130600612788. PMID 17008258.
- Graham, S.E. & Matthews, M.D. (2000). Modeling and measuring situation awareness. In J.H. Hiller & R.L. Wampler (Eds.), Workshop on assessing and measuring training performance effectiveness (Tech. Rep. 1116) (pp. 14–24). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.
- Hartel, C.E.J., Smith, K., & Prince, C. (1991, April). Defining aircrew coordination: Searching mishaps for meaning. Paper presented at the 6th International Symposium on Aviation Psychology, Columbus, OH.
- Harwood, K., Barnett, B., & Wickens, C.D. (1988). Situational awareness: A conceptual and methodological framework. In F.E. McIntire (Ed.), Proceedings of the 11th Biennial Psychology in the Department of Defense Symposium (pp. 23–27). Colorado Springs, CO: U.S. Air Force Academy.
- Jeannot, E., Kelly, C. & Thompson, D. (2003). The development of situation awareness measures in ATM systems. Brussels: Eurocontrol.
- Jones, D.G. & Endsley, M.R. (2000). Examining the validity of real-time probes as a metric of situation awareness. Proceedings of the 14th Triennial Congress of the International Ergonomics Association and the 44th Annual Meeting of the Human Factors and Ergonomics Society. Santa Monica, CA: Human Factors and Ergonomics Society. https://web.archive.org/web/20070928120133/http://www.satechnologies.com/Papers/pdf/HFES2000-probes.pdf
- Klein, G.; Moon, B; Hoffman, R.R. (2006). "Making sense of sensemaking 1: Alternative perspectives". IEEE Intelligent Systems. 21 (4): 70–73. doi:10.1109/mis.2006.75.
- Kozlowski, S.W.J. (1998). Training and developing adaptive teams: Theory, principles, and research. In J.A. Cannon-Bowers, & E. Salas, (Eds.), Making decisions under stress: Implications for individual and team training (pp. 115–153). Washington, DC: American Psychological Association.
- Lundberg, J. (2015). Situation Awareness Systems, States and Processes: A holistic framework. Theoretical Issues in Ergonomics Science. doi: 10.1080/1463922X.2015.1008601
- Matthews, M.D., Pleban, R.J., Endsley, M.R., & Strater, L.G. (2000). Measures of infantry situation awareness for a virtual MOUT environment. Proceedings of the Human Performance, Situation Awareness and Automation: User-Centered Design for the New Millennium. Savannah, GA: SA Technologies, Inc.
- Merket, D.C., Bergondy, M., & Cuevas-Mesa, H. (1997, March). Making sense out of teamwork errors in complex environments. Paper presented at the 18th Annual Industrial/Organizational-Organizational Behavior Graduate Student Conference, Roanoke, VA.
- Moray, N. (2004). Ou sont les neiges d'antan? ("Where are the snows of yesteryear?"). In D.A. Vincenzi, M. Mouloua & P.A. Hancock (Eds), Human performance, situation awareness and automation: Current research and trends (pp. 1–31). Mahwah: LEA.
- Nullmeyer, R.T., Stella, D., Montijo, G.A., & Harden, S.W. (2005). Human factors in Air Force flight mishaps: Implications for change. Proceedings of the 27th Annual Interservice/Industry Training, Simulation, and Education Conference (paper no. 2260). Arlington, VA: National Training Systems Association.
- Press, M. (1986). Situation awareness: Let's get serious about the clue-bird. Unpublished manuscript.
- Salas, E., Dickinson, T.L., Converse, S., & Tannenbaum, S.I. (1992). Toward an understanding of team performance and training. In R.W. Swezey & E. Salas (Eds.), Teams: their training and performance (pp. 3–29). Norwood, NJ: Ablex.
- Sarter, N.B.; Woods, D.D. (1991). "Situation awareness: A critical but ill-defined phenomenon". International Journal of Aviation Psychology. 1: 45–57. doi:10.1207/s15327108ijap0101_4.
- Schulz, CM; Endsley, MR; Kochs, EF; Gelb, AW; Wagner, KJ (Mar 2013). "Situation Awareness in Anesthesia - Concept and Research". Anesthesiology. 118 (3): 729–42. doi:10.1097/aln.0b013e318280a40f. PMID 23291626.
- Serfaty, D., MacMillan, J., Entin, E.E., & Entin, E.B. (1997). The decision-making expertise of battle commanders. In C.E. Zsambok & G. Klein (Eds.), Naturalistic decision making (pp. 233–246). Mahwah, NJ: LEA.
- Smith, K.; Hancock, P.A. (1995). "Situation awareness is adaptive, externally directed consciousness". Human Factors. 37 (1): 137–148. doi:10.1518/001872095779049444.
- Sollenberger, R.L., & Stein, E.S. (1995). A simulation study of air traffic controllers' situation awareness. Proceedings of an International Conference: Experimental Analysis and Measurement of Situation Awareness (pp. 211–217). Daytona Beach, FL.
- Spick, M. (1988). The Ace Factor: Air Combat and the Role of Situational Awareness. Annapolis, MD: Naval Institute Press.
- Strater, L.D., Endsley, M.R., Pleban, R.J., & Matthews, M.D. (2001). Measures of platoon leader situation awareness in virtual decision making exercises (No. Research Report 1770). Alexandria, VA: Army Research Institute.
- Taylor, R.M. (1989). Situational awareness rating technique (SART): The development of a tool for aircrew systems design. Proceedings of the AGARD AMP Symposium on Situational Awareness in Aerospace Operations, CP478. Seuilly-sur Seine: NATO AGARD.
- Vidulich, M.A. (2000). Testing the sensitivity of situation awareness metrics in interface evaluations. In M.R. Endsley & D.J. Garland, (Eds.), Situation awareness analysis and measurement (pp. 227–246). Mahwah, NJ: Lawrence Erlbaum Associates.
- Watts, B.D. (2004). "Situation awareness" in air-to-air combat and friction. Chapter 9 in Clausewitzian Friction and Future War, McNair Paper no. 68 (revised edition; originally published in 1996 as McNair Paper no. 52). Institute of National Strategic Studies, National Defense University
- Wilson, G.F. (2000). Strategies for psychophysiological assessment of situation awareness. In M.R. Endsley & D.J. Garland, (Eds.), Situation awareness analysis and measurement (pp. 175–188). Mahwah, NJ: Lawrence Erlbaum Associates.
- Boddhu, Sanjay K., et al. (2012). "Increasing situational awareness using smartphones." SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2012.