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Situation awareness
Situation awareness
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Situational awareness or situation awareness, often abbreviated as SA is the understanding of an environment, its elements, and how it changes with respect to time or other factors. It is also defined as the perception of the elements in the environment considering time and space, the understanding of their meaning, and the prediction of their status in the near future.[1] It is also defined as adaptive, externally-directed consciousness focused on acquiring knowledge about a dynamic task environment and directed action within that environment.[2]

Situation awareness is recognized as a critical foundation for successful decision making in many situations, including the ones which involve the protection of human life and property, such as law enforcement, aviation, air traffic control, ship navigation,[3] health care,[4] emergency response, military command and control operations, transmission system operators, self defense,[5] and offshore oil and nuclear power plant management.[6]

Inadequate situation awareness has been identified as one of the primary causal factors in accidents attributed to human error.[7][8][9][10] According to Endsley’s situation awareness theory, when someone meets a dangerous situation, that person needs an appropriate and a precise decision-making process which includes pattern recognition and matching, formation of sophisticated frameworks and fundamental knowledge that aids correct decision making.[11]

The formal definition of situational awareness is often described as three ascending levels:

  1. Perception of the elements in the environment,
  2. Comprehension or understanding of the situation, and
  3. Projection of future status.[12]

People with the highest levels of situational awareness not only perceive the relevant information for their goals and decisions, but are also able to integrate that information to understand its meaning or significance, and are able to project likely or possible future scenarios. These higher levels of situational awareness are critical for proactive decision making in demanding environments.

Three aspects of situational awareness have been the focus in research: situational awareness states, situational awareness systems, and situational awareness processes. Situational awareness states refers to the actual level of awareness people have of the situation. Situational awareness systems refers to technologies that are developed to support situational awareness in many environments. Situational awareness processes refers to the updating of situational awareness states, and what guides the moment-to-moment change of situational awareness.[13]

History

[edit]

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.[14] The term can be traced to World War I, where it was recognized as a crucial skill for crews in military aircraft.[15]

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[16] and mental workload measurement[17] built on the concept of awareness measurement from a series of experiments that measured contingency awareness during learning,[18][19] and later extended to mental workload and fatigue.[20]

Situation awareness appears in the technical literature as early as 1983, when describing the benefits of a prototype touch-screen navigation display.[21] 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.

The term was first defined formally by Endsley in 1988.[22] 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.[23] They identified having good SA as the decisive factor in air combat engagements—the "ace factor".[24] 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, SA has gone far beyond the field of aviation to work being conducted in a wide variety of environments. SA is being studied in such diverse areas as air traffic control, nuclear power plant operation, emergency response, maritime operations, space, oil and gas drilling, vehicle operation, and health care (e.g. anesthesiology and nursing).[25][26][27][28][29][30][31]

Theoretical model

[edit]

Endsley's Cognitive Model of SA

[edit]
Endsley's model of SA. This is a synthesis of versions she has given in several sources, notably in 1995[32] and 2000.[33]
Endsley's model of SA. This is a synthesis of versions she has given in several sources, notably in 1995[32] and 2000.[33]

The most widely cited and accepted model of SA was developed by Dr. Mica Endsley,[25] which has been shown to be largely supported by research findings.[34] Lee, Cassano-Pinche, and Vicente found that Endsley's Model of SA received 50% more citations following its publication than any other paper in Human Factors compared to other papers in the 30 year period of their review.[35]

Endsley's model describes the cognitive processes and mechanisms that are used by people to assess situations to develop SA, and the task and environmental factors that also affect their ability to get SA. It describes in detail the three levels 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 shows how SA "provides the primary basis for subsequent decision making and performance in the operation of complex, dynamic systems".[36] 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.[37]

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.

Endsley's model of SA illustrates several variables that can influence the development and maintenance of SA, including individual, task, and environmental factors.

In summary, the model consists of several key factors that describe the cognitive processes involved in SA:[38]

  • 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.

The model also points to a number of features of the task and environment that affect SA:

  • The capability of the system and the user interface for conveying important information to the person in a way that is easy to integrate and process.
  • Both high workload and stress can negatively affect SA. Information overload is a problem in many situations.
  • Underload (vigilance conditions) can also negatively affect SA.
  • The complexity of the systems and situations a person is in can negatively affect SA by making it difficult to form accurate mental models.
  • Automation is a major factor reducing situation awareness in many environments (e.g. aviation, driving, power operations). See out of the loop performance problems. This is due to it creating situations where people are forced to become monitors which they are poor at (due to vigilance problems), often poor system transparency with needed information not provided, and an overall reduction in the level of cognitive engagement of people with automated systems.[39]

Experience and training have a significant impact on people's ability to develop SA, due to its impact on the development of mental models that reduce processing demands and help people to better prioritize their goals.[40] In addition, it has been found that individuals vary in their ability to acquire SA; thus, simply providing the same system and training will not ensure similar SA across different individuals. Research has shown that there are a number of factors that make some people better at SA than others including differences in spatial abilities and multi-tasking skills.[41]

Criticisms of SA

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Criticisms of the SA construct and the model are generally viewed as unfounded and addressed.[42][43][44] The Endsley model is very detailed in describing the exact cognitive processes involved in SA. A narrative literature review of SA, performance, and other human factors constructs states that SA “... is valuable in understanding and predicting human-system performance in complex systems.”[42]

Nevertheless, there are several criticisms of SA. One criticism is the danger of circularity with SA: “How does one know that SA was lost? Because the human responded inappropriately. Why did the human respond inappropriately? Because SA was lost.” [45] Building on the circularity concern, others deemed SA a folk model on the basis it is frequently overgeneralized and immune to falsification.[46][47] A response to these criticisms it arguing that measures of SA are “... falsifiable in terms of their usefulness in prediction.”[42]

A recent review and meta-analysis of SA measures showed they were highly correlated or predictive of performance, which initially appears to provide strong quantitative evidence refuting criticisms of SA.[44] However, the inclusion criteria in this meta-analysis[44] was limited to positive correlations reaching desirable levels of statistical significance.[48] That is, more desirable results hypothesis supporting results were included while the less desirable results, contradicting the hypothesis, were excluded. The justification was "Not all measures of SA are relevant to performance."[44] This an example of a circular analysis or double-dipping,[49] where the dataset being analyzed are selected based on the outcome from analyzing the same dataset.

Because only more desirable effects were included, the results of this meta-analysis were predetermined – predictive measures of SA were predictive.[48] Further, there were inflated estimates of mean effect sizes compared to an analysis that did not select results using statistical significance.[48] Determining the relevance of SA based on the desirability of outcomes and analyzing only supporting results is a circular conceptualization of SA and revives concerns about the falsifiability of SA.[48]

[edit]

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.[50] 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.[51][52][53] Sensemaking and achieving understanding are more commonly found in industry and the organizational psychology literature and often relate to achieving long-term strategic objectives.

There are also biological mediators of situational awareness, most notably hormones such as testosterone, and neurotransmitters such as dopamine and norepinephrine.[54]

Phase
Process Outcome
Objective Tactical (short-term) situational assessment situation awareness
Strategic (long-term) sensemaking understanding
Scientific (longer-term) analysis prediction

Situational understanding

[edit]

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".[55] 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.

Situational assessment

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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.[1] 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.[56]

Mental models

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Accurate mental models are one of the prerequisites for achieving SA.[22][57][58] A mental model can be described as a set of well-defined, highly organized yet dynamic knowledge structures developed over time from experience.[59][60] 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.[61] 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".[62][63] Cues in the environment activate these mental models, which in turn guide their decision making process.

Sensemaking

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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.[64]

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",[65] 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.[66][43] 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.[66][43]

In team operations

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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. 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.

— Salas et al. (1992)[67]

Team SA

[edit]

Team SA is defined as "the degree to which every team member possesses the SA required for his or her responsibilities".[38] 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. Therefore, team members need to be successful in communicating information between them (including how they are interpreting or projecting changes in the situation to form level 2 and 3 SA) or in each independently being able to get the information they need.

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. 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.[68]

Shared SA

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Shared situation awareness can be defined as "the degree to which team members possess the same SA on shared SA requirements".[69][70] 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 degree to which people have a common understanding on information that is in the overlap of the SA requirements of the team members. 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 creates information overload to sort through to get needed information.[71][72] It is only that information which is relevant to the SA requirements of each team member that needs to be shared.

Team SA model

[edit]

The situation awareness of the team as a whole, therefore, is dependent upon both a high level of SA among individual team members for the aspects of the situation necessary for their job; and 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.[73] Endsley and Jones[57][73] 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 processes

[edit]

In time-critical decision-making processes, swift and effective choices are imperative to address and navigate urgent situations. In such scenarios, the ability to analyze information rapidly, prioritize key factors, and execute decisions promptly becomes paramount. Time constraints often necessitate a balance between thorough deliberation and the need for quick action.

The decision-maker must rely on a combination of experience, intuition, and available data to make informed choices under pressure. Prioritizing critical elements, assessing potential outcomes, and considering the immediate and long-term consequences are crucial aspects of effective time-critical decision-making.

Furthermore, clear communication is essential to ensure that decisions are swiftly conveyed to relevant stakeholders and executed seamlessly. Collaborative efforts, streamlined processes, and well-defined protocols can enhance the efficiency of decision-making in time-sensitive situations.

Adaptability and the ability to recalibrate strategies in real-time are vital attributes in time-critical scenarios, as unforeseen developments may require rapid adjustments to the initial decision. Embracing technological advancements and data-driven insights, and incorporating simulation exercises, can also contribute to better decision-making outcomes in high-pressure situations.

Ultimately, successful time-critical decision-making involves a combination of expertise, preparedness, effective communication, and a willingness to adapt, ensuring that the chosen course of action aligns with the urgency of the situation while minimizing the risk of errors.

Measurement

[edit]

While the SA construct has been widely researched, the multivariate nature of SA poses a considerable challenge to its quantification and measurement.[a] 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.[74] These SA measurement approaches are further described next.

Objective measures

[edit]

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[75]), during an interruption in task performance (e.g., situation awareness global assessment technique (SAGAT),[32] 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

[edit]

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;[76] the situation awareness rating technique[77]). 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.[78]

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 measures

[edit]

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.[25] In other words, good SA does not always lead to good performance and poor SA does not always lead to poor performance.[79] 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.[b] 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

[edit]

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.[57] 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.[81][82] Although evidence exists to support the utility of communication analysis for predicting team SA,[83] 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.[84] 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 electroencephalography 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.[85] Other psycho-physiological measures, such as event-related potentials, event-related desynchronization, transient heart rate, and electrodermal activity, 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.[85] 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.[85]

Multi-faceted approach to measurement

[edit]

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.[c] 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.[86] Such a multi-faced approach to SA measurement capitalizes on the strengths of each measure while minimizing the limitations inherent in each.

Limitations

[edit]

Situation awareness is limited by sensory input and available attention, by the individual's knowledge and experience, and by their ability to analyse the available information effectively. Attention is a limited resource, and may be reduced by distraction and task loading. Comprehension of the situation and projection of future status depend heavily on relevant knowledge, understanding, and experience in similar environments. Team SA is less limited by these factors, as there is a wider knowledge and experience base, but it is limited by the effectiveness of communication within the team.[87]

Training

[edit]

Following Endsley's paradigm and with cognitive resource management model[88] with neurofeedback techniques, Spanish Pedagogist María Gabriela López García (2010) implemented and developed a new SA training pattern.[89] 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.[90]

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.[91]

On-the-job examples

[edit]

Emergency medical call-outs

[edit]

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[92] 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[93] 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.

Vehicle driving and aviation

[edit]

A loss in situational awareness has led to many transportation accidents, including the 1991 Los Angeles Airport runway collision[94] and the 2015 Philadelphia train derailment.[95]

Search and rescue

[edit]

Within the search and rescue context, situational awareness is applied primarily to avoid injury to search crews by 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.[96] Public safety agencies are increasingly using situational awareness applications like Android Tactical Assault Kit on mobile devices and even robots to improve situational awareness.[97]

Forestry crosscut saw / chainsaw

[edit]

In the United States Forest Service the use of chainsaws and crosscut saws requires training and certification.[98] A great deal of that training describes situational awareness as an approach toward environmental awareness but also self-awareness[99] 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

[edit]

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[100] while also being fully aware of what is happening around the officer in areas not currently the focus of an officer's immediate task.

Cybersecurity threat operations

[edit]

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,[101] network security,[102] or using collaborative approaches to improve the awareness of users.[103] There are also research efforts to automate the processing of communication network information in order to obtain or improve cyber-situational awareness.[104]

Situation awareness-based agency transparency model

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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 have been determined to discourage "human trust in autonomous systems, with 'low observability, predictability, directability and auditability' and 'low mutual understanding of common goals' being among the key issues."[105] The researchers at the US Army Research Laboratory designed three levels of situational awareness transparency based on Endsley's theory of perception, comprehension, and projection. The greater the level of situational awareness, they claimed, the more information the agent conveys to the user.[106]

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.[105]

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.[107]

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.[107] 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.[105] Unlike in the IMPACT research, the agent informing the user of its level of uncertainty in decision making, no increase in trust was observed.[107]

Strategies for Acquiring Situational Awareness

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Crowdsourcing

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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".[108][109][110][111][112][113][114][115] 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.[115] A crowdsourcing approach to sensing, particularly in crisis situations, has been referred to as crowdsensing.[116] Crowdmapping is a subtype of crowdsourcing[117][118] 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[119][120][121][122] that can improve situational awareness during an incident and be used to support incident response.[123]

Cloud-based geographic information system display of structured data

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A Cloud-based Geographic Information System (GIS) with a display of structured data refers to a system that utilizes cloud computing technology to store, manage, analyze, and visualize geographic data in a structured format. This approach offers several advantages, including accessibility, scalability, and collaboration, compared to traditional on-premises GIS systems.

Here's a breakdown of the key components:

Cloud-Based Infrastructure:

  • The GIS system is hosted on cloud servers, allowing users to access it over the internet. This eliminates the need for local installations and provides flexibility in terms of resource allocation and scalability.

Geographic Information System (GIS):

  • GIS is a framework for capturing, storing, analyzing, and displaying spatial or geographic data. It involves the use of maps and geographical information to understand relationships and patterns.

Structured Data Storage:

  • Geographic data, such as coordinates, boundaries, and attributes, is stored in a structured format within the cloud. This could involve databases or other storage solutions that allow for efficient retrieval and analysis.

Data Analysis and Processing:

  • The cloud-based GIS performs various analytical processes on the structured geographic data. This may include spatial analysis, overlay operations, and statistical calculations to derive meaningful insights.

Visualization Tools:

  • The system includes tools for visualizing geographic data in the form of maps, charts, and graphs. Users can interact with the data visually, making it easier to comprehend complex spatial relationships.

Collaborative Features:

  • Cloud-based GIS often facilitates collaboration among multiple users. Team members can access and work on the same geographic data simultaneously, fostering teamwork and information sharing.

Real-Time Updates:

  • Cloud-based systems enable real-time updates to the geographic data. As new information becomes available, it can be seamlessly integrated into the system, ensuring that users always have access to the most current data.

Integration with Other Cloud Services:

  • Cloud-based GIS can integrate with other cloud services, such as data storage, processing, and analytics services. This interoperability enhances the overall capabilities of the system.

Overall, a cloud-based GIS with structured data display provides a dynamic and efficient platform for managing geographic information, making it accessible, scalable, and collaborative for a wide range of applications, from urban planning and environmental monitoring to business analytics and disaster response.

Military training methods

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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.

See also

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Notes

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References

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Sources

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  • Banbury, S.; Tremblay, S. (2004). A cognitive approach to situation awareness: Theory and application. Aldershot, UK: Ashgate Publishing. pp. 317–341.
  • Endsley, M.R. (1995a). "Measurement of situation awareness in dynamic systems". Human Factors. 37 (1): 65–84. doi:10.1518/001872095779049499. S2CID 207496393.
  • Endsley, M.R. (1995b). "Toward a theory of situation awareness in dynamic systems". Human Factors. 37 (1): 32–64. doi:10.1518/001872095779049543. S2CID 8347993.
  • 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.
  • Flach, J.M. (1995). "Situation awareness: Proceed with caution". Human Factors. 37 (1): 149–157. doi:10.1518/001872095779049480. S2CID 10159068.
  • 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. S2CID 12538674.
  • Smith, K.; Hancock, P.A. (1995). "Situation awareness is adaptive, externally directed consciousness". Human Factors. 37 (1): 137–148. doi:10.1518/001872095779049444. S2CID 45587115.

Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Situation awareness (SA), also known as situational awareness, is the perception of environmental elements within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. This foundational definition stems from psychologist Mica R. Endsley's three-level model of SA. Endsley's model structures SA into three hierarchical levels that build upon one another to support effective in dynamic environments. Level 1 involves the basic of relevant elements, such as instruments or events in the surroundings. Level 2 entails comprehension, where perceived data is integrated and understood in the context of current goals. Level 3 focuses on projection, anticipating future developments based on the comprehended situation to inform actions. These levels emphasize SA as a cognitive process influenced by factors like , stress, , and system design complexity. SA is critical in high-stakes domains requiring rapid responses to changing conditions, where lapses contribute significantly to human errors. In , for instance, poor SA has been implicated in over 200 accidents, with 77.4% of flight crew errors occurring at the level and 10.4% of air traffic controller errors at the projection level. Applications extend to , where anesthesiologists rely on SA for monitoring; land-based industries like power generation and process control; and emergency response, including . Enhancements through targeted training, such as programs, and ergonomic system designs aim to mitigate SA errors and improve safety across these fields.

Definition and Fundamentals

Core Components

Situation awareness (SA) is defined as the of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future, representing a dynamic process essential for effective in complex systems. This process encompasses three core components: (Level 1 SA), which involves detecting relevant environmental cues; comprehension (Level 2 SA), which interprets those cues; and projection (Level 3 SA), which anticipates future developments based on current understanding. Perception, the foundational component of SA, entails the detection and recognition of salient elements from the surrounding environment through various sensory inputs and data sources. These cues can include visual indicators such as displays or gauges, auditory signals like alarms, tactile feedback from tools, or direct observations of physical changes, all of which provide raw data about the system's state. In dynamic settings, perception relies on the individual's ability to identify critical information amid competing stimuli, where failure to detect cues accounts for a significant portion of SA errors—approximately 76% in aviation incidents. The role of is central to effective , as it determines which cues are prioritized and processed, particularly in environments characterized by or noise. is influenced by salience, where highly noticeable or goal-relevant features—such as flashing lights or sudden sounds—naturally draw focus, while less obvious cues require deliberate monitoring guided by and task objectives. For instance, studies indicate that about 35% of failures occur due to inattention to available data, despite its presence. In practical scenarios, perceptual cues manifest as observable indicators of potential issues; for example, in a workspace, an operator might detect a machine's unexpected stop or a warning light signaling a fault, allowing timely response to prevent disruptions or hazards. Such detection is fundamental to Endsley's model, which frames as the initial step in building SA (detailed further in theoretical models).

Levels of Situation Awareness

Situation awareness is commonly conceptualized as a hierarchical process comprising three levels that progressively build from basic to advanced predictive understanding, enabling individuals to interact effectively with dynamic environments. This framework posits that achieving higher levels of awareness requires successful processing at preceding levels, forming a foundational structure for in complex systems such as , operations, and response. Level 1, perception of elements in the environment, involves the detection and basic recognition of relevant entities, events, and states within the current situation. This foundational stage focuses on gathering raw sensory data, such as identifying positions on a or noting changes in patient during medical , without yet interpreting their significance. Failures here, often due to attentional limitations or poor interface design, result in incomplete or inaccurate input that undermines subsequent processing. Building on perceptual data, Level 2, comprehension of the current situation, entails integrating these elements into a coherent understanding of their meaning and to ongoing goals. At this stage, individuals assess how perceived information aligns with expectations and objectives, for instance, recognizing that a sudden altitude drop signals an immediate in piloting. Mental models—internal representations of —play a key role in this integration, facilitating and prioritization of salient features. Level 3, projection of future status, represents the highest tier, where individuals anticipate how the current situation will evolve based on comprehended elements and established patterns. This predictive reasoning allows for proactive responses, such as forecasting collision risks from converging trajectories in , emphasizing the use of to simulate near-term outcomes. Effective projection enhances performance in time-critical scenarios by enabling foresight beyond immediate observations. The levels are interdependent, with each relying on the accuracy and completeness of the prior stages; disruptions at Level 1, such as missed cues, propagate errors to comprehension and projection, potentially leading to cascading failures in overall awareness. This sequential hierarchy underscores the need for robust perceptual inputs to support meaningful interpretation and reliable forecasting in dynamic contexts.

Historical Development

Early Origins

The concept of situation awareness traces its early psychological roots to late 19th-century research on and . , in his seminal work (1890), described attention as the selective focusing of consciousness on specific aspects of the environment amid competing stimuli, emphasizing its role in grasping relevant details for adaptive behavior. This foundational idea of directed awareness in dynamic settings laid groundwork for later understandings of how individuals and integrate environmental . Similarly, , emerging in the early , stressed holistic where the whole situation is comprehended beyond isolated elements, influencing concepts of integrated environmental scanning. In , these ideas began manifesting practically during , particularly through the experiences of fighter pilots who stressed maintaining a "big picture" view to evade tactical errors and detect threats. German ace Oswald Boelcke's (1916), a set of eight rules for , highlighted the need for constant vigilance, such as always turning toward an enemy and avoiding surprise by scanning surroundings, which Gilson (1995) identifies as an early articulation of situational awareness principles. Pilots described this as an intuitive grasp of the overall aerial environment, essential for survival in chaotic dogfights, predating formal terminology but underscoring the perceptual integration of position, speed, and enemy movements. By the 1940s, explicitly incorporated environmental scanning into pilot training. The U.S. Army Air Forces' Field Manual 1-15: Tactics and Technique of Air Fighting (1942) instructed pilots to maintain constant through systematic scanning of the sky, particularly the upper rear hemisphere and below, to secure formations and prevent ambushes, while commanders analyzed enemy dispositions for broader situational comprehension. Similarly, the Royal Air Force's Pamphlet 117, Air Sense: Some Thoughts for Pilots at the EFTS (1943), defined "air sense" as the ability to anticipate dangers and react without delay by developing a comprehensive grasp of the flying environment, training elementary pilots to integrate sensory cues for effective decision-making. These doctrines reflected pre-formalized efforts to cultivate perceptual skills amid escalating demands.

Key Milestones and Contributors

The concept of situation awareness (SA) was formally introduced to the human factors literature in 1988 by Mica R. Endsley, who defined it as a pilot's internal model of the world around them, emphasizing its role in addressing cognitive overload and errors in complex systems. In this work, Endsley linked SA deficiencies directly to pilot errors, noting that incomplete or inaccurate awareness leads to flawed decision-making despite adequate training, particularly amid advanced and high-speed operations. Building on this foundation, Endsley published her seminal paper in , titled "Toward a Theory of Situation Awareness in Dynamic Systems," which established the widely influential three-level model of SA tailored to and other high-stakes domains. The model delineates SA as progressing from perception of environmental elements (Level 1), to comprehension of their significance (Level 2), and finally to projection of future states (Level 3), positioning SA as integral to goal-directed decision-making under workload, stress, and influences. In the 1990s, John M. Flach advanced an ecological approach to SA, critiquing individualistic models and emphasizing across human-machine systems, as articulated in his 1995 paper "Situation Awareness: Proceed with Caution." Flach drew on to argue that SA emerges from dynamic interactions between agents and their environments, rather than solely internal mental states, influencing subsequent research on adaptive, context-sensitive awareness in complex operations. A key milestone occurred in 2000 with the Human Performance, Situational Awareness and Automation Conference (HPSAA), which integrated SA principles into military (C2) systems through discussions on C4I environments and battlefield applications. This event highlighted SA's role in enhancing operational effectiveness in distributed C2 scenarios, informing NATO-aligned frameworks for information sharing and decision support. In the 2020s, advancements have incorporated (VR) into SA training, with studies demonstrating significant improvements in safety awareness and ; for instance, a 2024 quasi-experimental trial found VR-based programs increased safety knowledge by 25% and training efficacy by 30% in Industry 4.0 contexts compared to traditional methods. These updates extend SA applications to immersive simulations for and occupational safety, fostering proactive cognition in dynamic, technology-rich environments.

Theoretical Models

Endsley's Cognitive Model

Mica Endsley's cognitive model of situation awareness (SA), first introduced in and elaborated in , posits SA as a dynamic perceptual-cognitive process rather than a static product, central to effective in complex, time-sensitive environments. The model delineates three hierarchical yet interdependent levels of SA, emphasizing how operators perceive, comprehend, and project information to maintain awareness amid evolving conditions. At Level 1: Perception of Elements in the Environment, individuals detect and attend to salient cues from the surroundings within the limits of available time and , such as identifying an aircraft's position on a . Failures here often stem from incomplete or inaccessible . Level 2: Comprehension of the Current Situation builds on perception by integrating disparate elements into a coherent understanding of their meaning and relevance to goals, for example, recognizing that converging traffic poses a collision . This level relies on mental models to interpret . Level 3: Projection of Future Status involves potential outcomes based on comprehension, such as anticipating an aircraft's trajectory to avoid conflicts, enabling proactive responses. Several factors influence the attainment and maintenance of SA across these levels. Individual elements include goals and expectations, which direct attention to relevant information; prior experience, which refines mental models for faster comprehension; and current workload, which can overload cognitive resources and degrade perception. System-related factors encompass , which may enhance perception if well-designed but erode higher-level SA through reduced monitoring; and interface design, which affects data visibility and integration ease. The model incorporates feedback loops to reflect its dynamic nature, as illustrated below in a textual :

Environment (Stimuli) → Level 1: [Perception](/page/Perception) → Level 2: Comprehension → Level 3: Projection → Decision/Response → Environment (Updated) ↑ Feedback (Goals, Mental Models) ↑ Feedback (Expectations, Integration) ↑ Feedback (Projections Refine [Perception](/page/Perception))

Environment (Stimuli) → Level 1: [Perception](/page/Perception) → Level 2: Comprehension → Level 3: Projection → Decision/Response → Environment (Updated) ↑ Feedback (Goals, Mental Models) ↑ Feedback (Expectations, Integration) ↑ Feedback (Projections Refine [Perception](/page/Perception))

This structure shows bidirectional influences: higher levels feed back to guide lower-level processing (e.g., projections alerting operators to monitor specific cues), while ongoing environmental changes perpetually update the cycle, supported by mental models that store . Empirical validation of the model derives from studies analyzing incident reports, where SA errors predict operational failures. In a 1996 analysis of NASA's Aviation Safety Reporting System data, 76% of pilot SA errors occurred at Level 1 due to perception failures from inattention or poor data access; 20% at Level 2 from comprehension lapses like flawed mental models; and the remainder at Level 3 from projection shortcomings. These distributions align with the model's , demonstrating that addressing perceptual issues yields the greatest error reduction in high-stakes contexts.

Alternative and Extended Models

The ecological model of situation awareness, rooted in James J. Gibson's , posits that SA emerges directly from the coupling of and action within the environment, without reliance on internal mental representations. This perspective, advanced by John Flach, views SA as a dynamic embedded in the human-machine-environment interaction, where affordances—action possibilities offered by the surroundings—guide in real time. Unlike representational models, it emphasizes the organism's direct pickup of meaningful information from the ambient optic array, enabling situationally appropriate responses without intermediate cognitive constructs. Distributed situation awareness (DSA) models shift the focus from cognition to system-level , proposing that SA arises from interactions among human and non-human agents within a socio-technical . Developed by Neville Stanton and colleagues in , DSA conceptualizes awareness as a network of compatible but unique propositions held by each agent, shaped by their roles and information exchanges, rather than a shared state. This approach highlights how and artifacts contribute to overall awareness, addressing the limitations of person-centric views in multifaceted environments. In the 2010s, extensions to SA models incorporated to enable probabilistic projection of future states, enhancing predictive capabilities in , dynamic contexts. For instance, multi-entity Bayesian networks (MEBNs) model SA by integrating prior knowledge with observational evidence to forecast situational developments, allowing for quantified in projections. These frameworks extend traditional models by treating projection as a computational inference process, where beliefs are updated iteratively based on incoming . These alternative models address shortcomings in individual-focused frameworks, such as those emphasizing internal processes, by better accommodating the complexity of distributed, technology-mediated systems. The ecological approach counters representational biases by prioritizing direct environmental attunement, while DSA reveals how incompatibilities in agent awareness can degrade performance in evolving scenarios. Bayesian extensions further mitigate projection errors in high-variability settings, offering scalable tools for real-time adaptation.

Situational Understanding and Assessment

Situational understanding represents a deeper layer of interpretation that extends beyond the basic and comprehension of environmental elements, incorporating the broader , causal relationships, and potential implications of observed events. This process involves integrating sensory data with prior to form a coherent picture of the situation's significance, enabling individuals to discern patterns and anticipate developments. For instance, in dynamic operational settings, situational understanding allows operators to not only notice changes in their surroundings but also to evaluate how those changes relate to overarching goals or risks, thereby informing more nuanced responses. Situational assessment, in contrast, entails a systematic evaluation of the current environment to identify threats, opportunities, and vulnerabilities, often employing structured analytical tools to quantify and prioritize elements. In military and operational contexts, this frequently involves frameworks such as , which categorizes internal strengths and weaknesses alongside external opportunities and threats to guide . This evaluative approach helps decision-makers assess the balance of forces or resources, determining the feasibility of actions like advances or retreats based on a balanced of factors. While closely related, situational awareness differs from these processes in its emphasis on an ongoing, dynamic cycle of , comprehension, and projection, as opposed to the more episodic and deliberate nature of understanding and assessment. Situation awareness maintains a continuous state of about the environment, tying into the comprehension level of established models where elements are interpreted for immediate , whereas assessment is analytical and periodic, focusing on deliberate rather than real-time monitoring. In , for example, during missions, situational assessment might involve analyzing enemy positions to evaluate dispositions, activities, and movements, thereby assessing implications for tactical maneuvers without encompassing the full perceptual loop of awareness.

Mental Models and Sensemaking

Mental models serve as cognitive representations of systems, environments, and their interrelationships, enabling individuals to comprehend current states, anticipate changes, and project future outcomes in dynamic settings. These internal schemas, drawn from and domain expertise, filter incoming perceptual data by prioritizing relevant cues and integrating them with prior to form a coherent understanding. In the context of situation awareness (SA), mental models facilitate the transition from raw to higher-level comprehension and prediction, particularly for experts who possess more refined and interconnected representations compared to novices. Sensemaking, as conceptualized by Weick, involves the retrospective and prospective framing of ambiguous events to impose coherence and enable action amid . Retrospectively, individuals interpret past data and experiences to construct plausible narratives; prospectively, they test these frames through ongoing interactions with the environment, refining understandings iteratively. This process creates meaning from equivocal information, supporting sustained SA by resolving discrepancies between expectations and reality in fluid contexts. The role of mental models in SA is amplified through schema theory, where pre-existing knowledge structures guide the activation, modification, and application of cognitive templates to interpret situational elements. Schemas act as filters that organize perceptions and anticipate trajectories, allowing for rapid adaptation in complex environments; for instance, in incident command during crises like the 2019 Utrecht terrorist attack, commanders evolved narratives by integrating interdependent cues from operational levels, updating schemas to shift from an initial marauding threat frame to a more accurate lone-actor assessment, thereby enhancing collective coherence.

Team and Shared Situation Awareness

Individual vs. Team Dynamics

Individual situation awareness operates as a personal cognitive process involving the of environmental elements within a volume of time and space, the comprehension of their meaning, and the projection of their future status, heavily influenced by an individual's prior , expertise, and mental models. This cycle enables solo operators to make informed decisions in dynamic environments, such as pilots monitoring instruments or clinicians assessing patient vitals, but it remains confined to the individual's internal representation without inherent mechanisms for external validation or adjustment. In contrast, team situation awareness arises from the dynamic interplay among group members, where individual awareness levels must overlap and align to support collective goals, creating an interdependent structure that amplifies overall effectiveness beyond what isolated individuals can achieve. This requires compatibility in how team members perceive, interpret, and anticipate elements of the situation, often facilitated through coordinated roles and shared resources, as seen in surgical teams where surgeons, nurses, and anesthesiologists integrate their perspectives for seamless operations. Unlike individual SA, team dynamics emphasize the distribution of awareness across members, allowing for that leverages diverse expertise but demands ongoing synchronization to prevent fragmentation. Key challenges in team settings include communication gaps that disrupt the flow of critical , leading to misaligned awareness and potential coordination failures, such as in response where incomplete updates result in duplicated efforts or overlooked threats. These gaps often stem from hierarchical barriers, noise in high-stress contexts, or differing interpretations based on role-specific focuses, undermining the compatibility needed for effective team performance. Empirical studies in simulated high-stakes environments demonstrate the value of team SA, with in emergency departments showing that formal training incorporating SA principles reduces clinical errors from 30.9% to 4.4% compared to baseline individual-oriented approaches, highlighting a substantial improvement in collective accuracy and . Such findings underscore how aligned can mitigate risks more effectively than solo efforts, particularly in time-sensitive scenarios.

Models of Shared SA

Mica Endsley's model of situation awareness, originally focused on individual , was extended in to encompass team contexts, emphasizing shared situation awareness (SA) as the degree to which team members possess compatible understandings of critical elements necessary for coordinated performance. This extension highlights how compatible mental models—internal representations of the environment and tasks—facilitate the synchronization of individual SAs into a collective projection of future states, enabling teams to anticipate and respond to dynamic changes effectively. In parallel, Eduardo Salas and colleagues proposed a team SA model in 1995 that portrays SA as a dynamic, cyclical emerging from team interactions. Key components include shared understanding, achieved through communication and integration of individual perceptions; coordinated action, where aligned behaviors support task execution; and feedback loops, which allow continuous refinement of the team's common picture to adapt to evolving situations. This framework underscores the role of preexisting knowledge structures in initiating and sustaining team-level SA. Frameworks of shared SA often delineate levels of sharedness to describe how individual cognitions integrate hierarchically. Individual SA operates at the personal level, focusing on unique perceptions and projections relevant to one's . Compatible SA emerges when team members' individual SAs overlap sufficiently on mission-critical elements, supported by shared mental models without requiring identical knowledge. Shared SA represents the highest level, where the team maintains a unified operational picture of all relevant aspects, achieved through explicit mechanisms like communication. Empirical evidence from supports the link between shared SA and mission success, particularly in environments. Similarly, research on modern aircrews has shown that enhanced shared SA, facilitated by tools like shared displays, reduces errors and boosts performance outcomes in simulated high-stakes scenarios.

Applications in

Time-Critical Environments

Time-critical environments, such as and combat operations, are characterized by high-velocity changes, significant uncertainty, and elevated stakes where delays in response can lead to catastrophic outcomes. In , controllers must perceive and project the trajectories of multiple moving at high speeds in three-dimensional space, often with incomplete data due to limitations or communication gaps, demanding rapid comprehension to maintain safe separations. Similarly, in combat settings, soldiers face rapidly evolving threats amid noise, poor visibility, and psychological stressors like fear, which heighten the risk of errors if situational awareness (SA) falters. These conditions require operators to process dynamic in seconds, where even brief lapses can result in collisions or engagements with non-threats. The role of SA in these environments centers on enabling quick perception of key elements to prevent "tunnel vision"—a narrowing of attention that ignores peripheral cues—and to support projection of future states under intense time pressure. Effective SA counters attentional narrowing by integrating multiple sensory inputs, such as visual displays and auditory alerts, allowing operators to anticipate conflicts before they escalate. For instance, in Endsley's cognitive model, the projection level of SA is particularly vital here, as it facilitates forecasting imminent dangers like aircraft incursions or enemy movements within compressed decision timelines. Without this, operators risk overlooking critical changes, leading to performance degradation in high-uncertainty scenarios. A notable example of SA lapses in time-critical combat occurred during the 1991 Gulf War air campaigns, where friendly fire incidents accounted for approximately 17% of U.S. casualties, including 35 deaths and 72 wounds from misidentification under rapid maneuvers and poor visibility. Battlefield investigations highlighted poor SA and target identification as primary causes, particularly in night operations involving A-10 aircraft striking British vehicles or ground forces engaging allies due to disorientation in flat terrain obscured by smoke and sandstorms. These events, such as the attack on 37 British Warrior infantry fighting vehicles that killed 9 and wounded 11, underscored how uncertainty and velocity overwhelmed perception, resulting in "blue-on-blue" engagements that could have been mitigated with better real-time awareness. To address these challenges, strategies in time-critical environments emphasize prioritization of salient cues within the OODA (Observe-Orient-Decide-Act) loop framework, where SA enhances the observation and orientation phases to filter relevant information amid overload. This integration allows operators, such as pilots or controllers, to focus on high-threat indicators—like proximity alerts or incoming fire—while deprioritizing noise, thereby accelerating decision cycles without succumbing to complacency or inattention. In combat simulations, training that aligns SA cue prioritization with OODA has reduced Level 1 errors, which constitute the majority of incidents in urban assaults and patrols.

High-Stakes Operational Contexts

High-stakes operational contexts, such as coordination and surgical procedures, demand sustained situation awareness (SA) over extended periods, often spanning multiple hours amid evolving threats and resource constraints. In , operators in command centers manage multi-hour missions involving dynamic hazards like shifting weather patterns or expanding affected areas, requiring continuous monitoring of incoming data from field teams and sensors to maintain operational coherence. Similarly, in surgical environments like , teams navigate prolonged interventions where unforeseen complications, such as or anatomical variations, necessitate ongoing vigilance to prevent catastrophic errors. Maintaining SA in these settings is challenged by factors like operator fatigue, which degrades perceptual accuracy and predictive capabilities during long shifts, yet tools such as integrated dashboards facilitate sustained comprehension by aggregating for threat projection and . For instance, in military command centers, cognitive models emphasize the use of structured analyses like METT-T (Mission, , Troops, /, Time) to support prolonged SA, enabling commanders to anticipate evolving battle scenarios despite cognitive strain. In surgery, briefings and checklists serve as analogous aids, helping teams project procedural outcomes while countering fatigue-induced lapses in attention. These mechanisms underscore the need for adaptive supports to preserve Level 3 SA—future-oriented projection—essential for endurance in resource-intensive operations. A prominent case illustrating SA failures in such contexts is the 2010 , where drill crew monitoring deficiencies allowed a well blowout to escalate unchecked, resulting in 11 deaths and massive environmental damage. The crew misinterpreted pressure anomalies during negative pressure tests as benign "bladder effects," failing to project the influx of hydrocarbons due to flawed mental models and distractions like shift changes, which compromised sustained oversight over hours of testing. This incident highlights how lapses in Level 1 () and Level 2 (comprehension) SA in high-consequence drilling operations can cascade into , as detailed in post-accident analyses. In these environments, robust SA directly informs strategic by enabling operators to weigh uncertainties—such as incomplete threat in coordination—against high-consequence outcomes, often through shared SA among teams to align actions. For example, in prolonged responses, AI-enhanced dashboards resource needs based on evolving , guiding allocations that mitigate escalation risks in uncertain terrains. Ultimately, effective SA integration fosters resilient strategies, transforming fragmented perceptions into cohesive plans that safeguard lives and assets in protracted, high-stakes scenarios.

Measurement Approaches

Objective and Performance-Based Methods

Objective measures of situation awareness (SA) focus on empirical, observable data derived from operator behavior and task outcomes, providing quantifiable indicators without relying on self-reports. Eye-tracking techniques, for instance, assess the level of SA by monitoring visual patterns, such as fixation duration and distribution, to determine how operators scan and attend to relevant environmental elements. In simulations, eye-tracking metrics like the visual sampling score—calculated as the percentage of critical events fixated within a short time window—have demonstrated strong correlations with overall performance, with Pearson's r values reaching 0.78 in studies involving 86 participants monitoring dynamic displays. These methods offer real-time, non-intrusive insights into perceptual processes, revealing deficiencies in allocation that precede errors. Response times serve as an objective proxy for the comprehension level of SA, measuring the latency between stimulus detection and appropriate interpretation or action initiation. In dynamic task environments, such as simulations, longer response times to projected events indicate poorer comprehension of situational implications, often correlating with increased and reduced decision accuracy. For example, in probe-based assessments, response latencies to comprehension queries have been shown to predict remaining task actions, with slower times linked to higher error potential in handling en route conflicts. This approach emphasizes the temporal dynamics of SA, where delays signal integration failures across perceptual inputs. Performance mapping links SA directly to operational outcomes, evaluating how well awareness translates into success metrics like error rates and mission completion in controlled simulations. In pilot training scenarios, low SA has been associated with elevated collision risks and target miss rates, while high SA correlates with improved tactical decisions, such as threefold increases in successful engagements. The Situation Awareness Global Assessment Technique (SAGAT), a seminal probe method, operationalizes this by freezing simulations at random intervals, blanking displays, and querying operators on Endsley's levels of , comprehension, and projection using predefined SA requirements from task analyses. SAGAT scores, derived from query accuracy, exhibit high reliability (test-retest correlations of 0.92–0.99) and for . Validation studies in underscore the efficacy of these objective methods, particularly in pilots, where SAGAT and related metrics explain substantial variance in performance—up to 74% in air traffic scenarios involving future event projections. In flight simulations, operators with superior SAGAT-assessed SA demonstrated significantly lower error rates and higher success in threat neutralization, establishing these techniques as robust predictors of real-world operational effectiveness. Such correlations highlight the practical utility of objective measures for identifying SA gaps and informing system design enhancements.

Subjective and Process-Oriented Techniques

Subjective measures of situation awareness (SA) rely on individuals' self-reports to gauge their perceived levels of awareness, offering insights into personal experiences that may not be captured through external observations. The Situation Awareness Rating Technique (), developed for systems design, is a prominent subjective tool consisting of a with ten bipolar rating scales assessing dimensions such as the stability of the situation, information quantity, and concentration required. Participants rate each dimension on a 7-point scale, with three global metrics—demand on attentional resources, supply of attentional resources, and understanding of the situation—used to compute an overall SA score via the formula SA = Understanding × (Supply / Demand). This technique allows operators to retrospectively evaluate their SA post-task, providing a quick and non-intrusive method applicable in and other dynamic environments. Process-oriented techniques emphasize the ongoing cognitive processes underlying SA, capturing how awareness evolves in real time rather than static outcomes. Think-aloud protocols involve participants verbalizing their thoughts during task performance, enabling researchers to trace the development of perception, comprehension, and projection of environmental elements as defined in established SA models. These protocols reveal the dynamic sensemaking process, such as how operators integrate incoming data to form mental models, and have been applied in domains like emergency response to identify comprehension gaps. By recording unprompted narration, think-aloud methods provide qualitative data on the evolution of SA, though they require minimal training to ensure natural responses. A specific process-oriented approach involves level-specific probe techniques, such as real-time queries tailored to the three levels of SA—perception (Level 1), comprehension (Level 2), and projection (Level 3)—posed during task execution to elicit immediate responses on awareness elements. For instance, Level 3 probes might ask operators to anticipate future system states based on current cues, tracking predictive aspects of SA without freezing the scenario. These probes, derived from task analyses, offer targeted insights into process dynamics and have shown moderate validity in correlating with offline measures in simulated operations. Subjective and process-oriented techniques excel in capturing nuanced aspects of SA, such as operators' confidence in their understanding and the temporal flow of cognitive integration, which can inform system design and interventions. They are particularly advantageous for their low cost, ease of administration, and ability to reflect internal states like attentional allocation without disrupting primary tasks excessively. However, these methods are prone to biases, including overconfidence or post-hoc rationalization, and may lack objectivity, as self-reports can be influenced by perceived performance rather than actual SA levels. Additionally, think-aloud and probe techniques risk altering natural behavior through verbalization demands, potentially inflating workload in high-stakes settings. Adaptations for team SA, such as coordinated probes, extend these methods but require careful synchronization to avoid inter-team biases.

Limitations and Challenges

Cognitive and Environmental Factors

Cognitive factors significantly impair situation awareness (SA) by taxing mental resources and altering perceptual processes. High cognitive workload, often arising from multitasking or complex information processing, inversely correlates with SA levels, as increased demands lead to reduced comprehension of environmental cues and poorer projection of future states. Stress exacerbates this through narrowed attention, where individuals fixate on immediate threats or primary tasks, limiting peripheral monitoring and overall situational comprehension. This phenomenon aligns with the Yerkes-Dodson law, which posits an inverted-U relationship between arousal and performance: moderate stress enhances vigilance and SA for simple tasks, but excessive stress induces hypervigilance, cognitive rigidity, and diminished cue utilization, thereby degrading SA in complex scenarios. Environmental factors further degrade SA by introducing uncertainties in information interpretation and system feedback. Information ambiguity, characterized by conflicting, incomplete, or unclear data streams, hinders accurate perception and comprehension, as operators struggle to resolve discrepancies without additional context, leading to erroneous mental models of the situation. Automation surprises compound this issue, occurring when automated systems exhibit opaque or unanticipated behaviors due to hidden states or mode transitions, resulting in a sudden loss of mode awareness and failure to intervene appropriately. These surprises erode SA by disrupting the operator's understanding of system intentions, often manifesting as errors of omission in high-autonomy environments like aviation cockpits. A prominent example of stress-induced impairment is attentional tunneling, where high-pressure conditions cause prolonged fixation on a single hypothesis or task, neglecting contradictory and broader environmental cues. This was evident in the 1979 Three Mile Island nuclear incident, where operators, under acute stress, tunneled on an erroneous stuck , overlooking critical indicators of core damage and exacerbating the partial meltdown. Interactions between cognitive and environmental factors amplify SA degradation, particularly through fatigue, which heightens susceptibility to distractions like noise. Fatigue reduces vigilance and working memory capacity, making individuals more vulnerable to environmental noise that overwhelms attentional resources and further impairs cue detection and integration. In noisy settings, fatigued operators experience compounded cognitive strain, leading to slower response times and heightened error rates in maintaining situational comprehension. These dynamics underscore how internal states like fatigue can intensify external interferences, creating a feedback loop that severely limits effective SA.

Criticisms of Traditional Frameworks

Traditional frameworks of situation awareness, particularly Mica Endsley's influential three-level model, have faced significant criticism for their overemphasis on individual at the expense of social, cultural, and distributed influences. Critics argue that these models portray SA primarily as a product of personal , comprehension, and projection, thereby overlooking how , communication, and cultural contexts shape the collective construction of situational understanding in real-world operations. For instance, Sidney Dekker and Micaëla Lützhöft contended that Endsley's approach reduces SA to an isolated mental process within the operator, ignoring the emergent, socially negotiated nature of awareness in high-reliability environments like , where interactions are essential for safety. Another key shortcoming highlighted in critiques is the static, hierarchical structure of these frameworks, which fails to capture the dynamic, non-linear processes inherent in complex, evolving environments. Endsley's levels—, comprehension, and —are often described as sequential stages, but this linearity does not adequately reflect the iterative, feedback-driven ways in which operators update their understanding amid rapid changes and uncertainties. and colleagues noted that such models impose an artificial rigidity on SA, underrepresenting the fluid interplay of , , and real-time that characterizes dynamic systems like emergency response or . Concerns have also been raised regarding the validity of measurement techniques associated with traditional frameworks, such as the Situation Awareness Global Assessment Technique (SAGAT), which relies on freezing simulations to probe operators' . Reviews in the have questioned SAGAT's artificiality, arguing that interrupting ongoing tasks disrupts natural cognitive flows and yields retrospective recollections prone to bias rather than genuine, in-situ awareness. Salmon et al. emphasized that SAGAT's reliance on predefined queries favors explicit, over implicit, distributed elements of SA, potentially invalidating its applicability in ecologically valid, uninterrupted scenarios. Alternative perspectives further challenge traditional views by positing SA as an or emergent property in complex sociotechnical systems, rather than a stable individual state. David Woods and colleagues have argued that in highly automated or interconnected environments, what appears as "loss of SA" often stems from systemic mismatches between human expectations and technological behaviors, rendering individual-focused models inadequate for explaining breakdowns. This commentary underscores that SA is not a fixed perceptual product but a provisional, context-dependent coordination that can mislead investigations if treated as a personal failing.

Training and Enhancement Strategies

Individual Training Methods

Individual training methods for situation awareness (SA) emphasize personal skill development through targeted simulations and cognitive exercises, aiming to enhance , comprehension, and projection without reliance on or external technologies. These approaches are particularly effective in high-stakes domains like and healthcare, where operators must independently process complex environments. Simulation-based training utilizes (VR) scenarios and high-fidelity simulators to conduct perception drills, allowing individuals to practice identifying and responding to dynamic cues in controlled settings. For instance, flight simulators replicate environments, enabling pilots to rehearse threat detection and under varying conditions, such as adverse weather or system failures. This method leverages immersive experiences to build familiarity with operational layouts and improve spatial orientation, with the flexibility to pause scenarios for reflection and to reinforce learning. Cognitive methods focus on internal processes, such as mental rehearsal—where individuals mentally simulate tasks to anticipate outcomes—and cue recognition exercises, which train the identification of salient environmental indicators. Mental rehearsal involves visualizing sequences of events to strengthen mental models, aiding in the projection of future states and reducing cognitive overload during actual operations. Cue recognition drills, often practiced through guided scenarios or , enhance the ability to match observed signals to expected patterns, drawing on stores to support rapid comprehension. These techniques target Endsley's levels of SA by building perceptual acuity and predictive reasoning at the individual level. Evidence from lab studies indicates that such individual training yields measurable SA improvements, with simulation-based interventions showing significant gains in performance metrics; for example, one study reported improvements in scores following video-based SA training compared to controls. Broader reviews highlight enhancements in error detection and recovery in controlled and medical simulations. A prominent example is the Federal Aviation Administration's (FAA) (CRM) program, which has incorporated dedicated SA modules since the early 1990s as part of its Advanced Qualification Program (AQP). These modules, evolving from second-generation CRM training around 1986, emphasize individual awareness skills like monitoring and , integrated into recurrent pilot training to mitigate factors risks.

Technological and Collaborative Tools

Technological tools have significantly advanced situation awareness (SA) by integrating digital enhancements directly into users' perceptual fields. (AR) overlays, for instance, superimpose critical cues such as navigational aids, threat indicators, or environmental data onto the real-world view via head-mounted displays or mobile devices, thereby reducing cognitive overload and improving real-time comprehension in dynamic settings. This approach has been shown to enhance decision-making in simulated operational tasks, as AR systems process sensor data to highlight salient features that might otherwise be overlooked. In and contexts, AR interfaces fuse live feeds with predictive annotations, enabling operators to maintain heightened vigilance without diverting attention from primary tasks. Artificial intelligence (AI) further augments SA through proactive alerts and forward projections, analyzing multimodal data streams—such as video, sensor inputs, and historical patterns—to anticipate events and notify users of potential risks. algorithms, including deep neural networks, generate these alerts by detecting anomalies in real time. In healthcare and applications, AI-driven projections model future states, such as patient deterioration or intruder trajectories, allowing for preemptive actions that elevate overall awareness levels. These tools prioritize salient information delivery, minimizing while supporting Endsley's levels of , comprehension, and projection. Collaborative tools leverage collective inputs to aggregate , fostering distributed SA across teams and communities. Crowdsourcing platforms, such as those utilizing and mobile apps, enable citizens and responders to contribute geotagged reports during emergencies, creating a shared informational that enhances situational comprehension. For example, during , these systems integrate user-submitted data with official sources to map affected areas, improving response coordination by providing dynamic, ground-level insights and reducing information latency. In , frameworks like mobile crowdsensing aggregate sensor data from smartphones to build comprehensive event timelines, benefiting both individual and team-level awareness without relying on centralized hierarchies. In military operations, cloud-based geographic information systems (GIS) facilitate structured data visualization, allowing commanders to overlay terrain, asset positions, and intelligence feeds on interactive maps for enhanced operational SA. These platforms support real-time collaboration by distributing geospatial layers across distributed teams, enabling synchronized projections of battlefield dynamics and resource allocation. Recent advancements in the 2020s, particularly drone swarms, have introduced shared sensory feeds that amplify team SA through decentralized networks. Swarm systems, as tested in U.S. Army exercises like EDGE22, allow multiple unmanned aerial vehicles to relay synchronized video and sensor data to operators, improving detection accuracy and interoperability in complex environments. This collective intelligence approach not only boosts immediate perception but also aids in projecting swarm behaviors for tactical advantages, with brief integration into shared SA models enhancing group-level understanding. As of 2025, emerging technologies like AI-assisted VR simulations continue to expand SA training frontiers, incorporating adaptive learning for personalized skill enhancement.

Real-World Examples

Aviation and Driving

In aviation, the 1977 Tenerife airport disaster serves as a seminal case study of situation awareness failure due to communication lapses. On March 27, two Boeing 747s—one from KLM and one from Pan Am—collided on the runway at Los Rodeos Airport, resulting in 583 fatalities, the deadliest accident in aviation history. The KLM captain initiated takeoff without explicit clearance after misinterpreting ambiguous air traffic control instructions amid foggy conditions and radio interference, leading to a loss of shared situation awareness between the pilots and controller; the crew failed to perceive the Pan Am aircraft still taxiing on the runway. This incident underscores how breakdowns in communication and perception, exacerbated by stress and non-standard phraseology, can cascade into catastrophic errors. In , distracted behaviors frequently erode situation awareness, contributing to a substantial portion of roadway crashes. The National Highway Traffic Safety Administration's (NHTSA) National Crash Causation Survey found that recognition errors—including inattention, internal and external distractions, and inadequate scanning—accounted for 41 percent of driver-related critical reasons in pre-crash scenarios. Such failures, a core component of level 1 situation awareness, often stem from activities like cellphone use or adjusting in-vehicle controls, impairing drivers' detection of hazards such as pedestrians or sudden braking vehicles. In 2021 alone, distraction-affected crashes resulted in 3,522 fatalities, highlighting the ongoing prevalence of these SA lapses in everyday mobility. Technological interventions like heads-up displays (HUDs) in automobiles address these vulnerabilities by overlaying navigational, speed, and information directly on the , minimizing eyes-off-road time and bolstering situation awareness. Unlike traditional dashboard displays, HUDs enable drivers to maintain visual focus on the forward environment, thereby enhancing comprehension and projection of traffic dynamics without diverting attention. Empirical studies confirm that HUDs reduce potential and improve overall , with evidence from simulator-based showing better detection and reduced compared to head-down alternatives. Aviation and driving share foundational principles for sustaining situation awareness, particularly in workload management, where excessive cognitive demands from multitasking or environmental stressors can degrade perceptual and predictive capabilities in both domains. Effective strategies, such as prioritizing critical and fostering clear communication in cockpits or solo monitoring in vehicles, mitigate these risks by distributing mental resources to prevent overload. These parallels emphasize the transferability of situation awareness concepts across transportation contexts to enhance safety outcomes.

Emergency Response and Law Enforcement

In emergency medical services, paramedics rely on situational awareness (SA) to perceive environmental cues, comprehend patient conditions, and project potential deteriorations during call-outs. This process enables them to anticipate critical changes, such as or hemodynamic instability, by integrating real-time data from , scene hazards, and patient history. A scoping review of literature highlights that effective SA is essential for mitigating risks to crews, patients, and bystanders, as lapses can lead to medical errors or injuries in dynamic prehospital environments. For instance, when managing an airway, a must recognize subtle signs of deterioration, like desaturation, to intervene proactively rather than reactively. Theoretical frameworks for SA in paramedicine emphasize the three-level model—perception, comprehension, and projection—to support anticipatory decision-making in high-stakes scenarios. During call-outs, paramedics assess not only the patient's immediate state but also external factors like traffic or bystander interference, projecting how these might exacerbate deterioration if unaddressed. Research underscores that robust SA allows paramedics to forecast events, such as a trauma patient's progression to shock, thereby optimizing resource allocation and treatment timelines. In , situational awareness is critical for assessing dynamic threats during vehicle pursuits, where officers must perceive suspect maneuvers, comprehend road conditions, and project evasion tactics to minimize risks to civilians and themselves. Guidelines for vehicular pursuits stress that familiarity with the pursuit area enhances overall SA, enabling better tactical decisions like termination or intervention. Officers evaluate factors such as density and in real time, using SA to balance apprehension goals with public safety. The 2014 illustrated policing failures in responses to civil demonstrations, where inadequate assessment led to escalation, including arrests of peaceful protesters and misjudged against non-threatening individuals. This contributed to unconstitutional practices and eroded public trust, as detailed in a U.S. Department of Justice investigation. In and operations, SA prevents accidents by attuning workers to environmental cues, such as instability or weather shifts, during high-risk tasks like felling. Non-technical skills studies in forestry operations identify SA as a key cognitive element, allowing operators to perceive hazards like kickback risks or falling debris and project safe cutting paths. In teams, maintaining SA involves integrating cues from the wilderness environment—such as tracks, weather patterns, or team fatigue—to avoid injuries and optimize subject location. Wilderness SAR protocols emphasize team-level awareness of micro-signals, like altered breathing rates, to detect emerging dangers and ensure operational safety. SA training in emergency response has demonstrated measurable benefits in simulations, with formal programs incorporating teamwork and awareness exercises reducing clinical errors by up to 75% in emergency department settings, according to systematic reviews of team training interventions. While specific FEMA simulation outcomes vary, U.S. Fire Administration guidance on SA enhancement through realistic drills supports error reduction by improving perception and projection skills among first responders. These strategies, including scenario-based exercises, foster adaptive responses in crisis environments, lowering the incidence of oversight-related incidents. For a recent example as of 2025, the January 2024 Flight 1282 incident highlighted SA challenges when a door plug blew out mid-flight, requiring rapid and comprehension by the crew to safely return, with investigations noting effective SA mitigated potential disaster.

Emerging Developments

AI and Automation Integration

AI systems enhance human situation awareness (SA) in hybrid human-AI environments by leveraging to support the projection level of SA, particularly in domains like autonomous vehicles. In autonomous driving, AI processes multimodal sensor data from , , and cameras using algorithms to forecast future environmental states, such as vehicle trajectories and potential hazards, enabling proactive decision-making for maneuvers like lane changes or collision avoidance. For instance, partially observable Markov decision processes (POMDPs) integrated into AI models predict uncertain behaviors at intersections, achieving near-optimal planning by projecting possible outcomes based on current perceptions. However, integrating AI and can challenge human SA through phenomena like and complacency, where over-reliance on AI reduces vigilance and perceptual monitoring. In the 2018 Uber self-driving vehicle incident in , the human safety operator's distraction—exemplified by streaming video on her phone for 34% of the trip, including glances away from the road just before impact—stemmed from automation complacency, preventing timely detection of a that the AI had identified 5.6 seconds earlier. This lapse in human perception, exacerbated by inadequate oversight mechanisms, highlights how can erode situational monitoring, contributing to fatal errors despite AI's capabilities. To mitigate these challenges, situation awareness-based agent transparency models have emerged to explain AI decisions, thereby preserving human SA in collaborative setups. The Situation Awareness-Based Transparency (SAT) framework, extended in the 2022 Situation Awareness Framework for Explainable AI (SAFE-AI), structures explanations across three levels: perception (what the AI observed), comprehension (why it acted), and projection (future implications), using techniques like counterfactuals to align human understanding with AI processes. Developed by Sanneman and , SAFE-AI employs the Situation Awareness Global Assessment Technique (SAGAT) to evaluate explanation efficacy, fostering trust and shared mental models in human-AI teams. Looking toward 2025, trends in explainable AI (XAI) emphasize shared SA between humans and machines through adaptive transparency mechanisms that dynamically provide decision rationales, enhancing team performance in high-stakes environments. Recent studies indicate that XAI aids, such as those disclosing environmental updates and AI reasoning, boost trust and situational comprehension in human-AI collaborations by up to 7.7 percentage points in task accuracy. These advancements, including autonomy-calibrated explanations, are increasingly adopted to balance AI efficiency with human oversight, as seen in evolving human-AI teaming protocols.

Situation Awareness in Cybersecurity

In cybersecurity, situation awareness (SA) refers to the comprehensive understanding of the current state of an organization's digital environment, including networks, systems, and data, as well as potential threats and vulnerabilities that could impact it. This involves three key levels adapted from Endsley's model: perceiving network anomalies such as unusual patterns or unauthorized access attempts; comprehending attack vectors, including the intent and implications of detected threats like or campaigns; and projecting escalations, such as forecasting how an intrusion might propagate to critical assets or lead to . Effective cyber SA enables security teams to make timely decisions in dynamic, high-stakes environments where threats evolve rapidly. Tools like (SIEM) systems play a central role in enhancing cyber SA by aggregating logs from diverse sources and providing real-time dashboards that visualize security events, , and risks. These platforms facilitate the and comprehension levels by alerting analysts to anomalies through automated rules and machine-readable outputs, allowing for quicker identification of sophisticated attacks. For instance, in the 2020 SolarWinds compromise, where Russian state actors inserted into software updates affecting thousands of organizations, including U.S. government agencies, SA failures were evident as most detection tools overlooked the subtle indicators, enabling the breach to persist undetected for up to nine months and highlighting gaps in perceiving and comprehending risks. In Security Operations Centers (SOCs), shared SA among team members is essential for coordinated responses to cyber incidents, fostering a through collaborative tools and communication protocols that distribute insights on threats across analysts, incident responders, and executives. Research on SOCs emphasizes that shared SA reduces response times by enabling collective comprehension of complex attack scenarios and joint projection of potential impacts, such as lateral movement in a network. This team-based approach is particularly vital in large-scale environments where individual analysts might miss interconnected threat signals. Emerging developments in 2024-2025 have seen AI-driven significantly bolster the projection level of cyber SA, with organizations using these tools shortening breach lifecycle times by an average of 80 days compared to those relying on traditional methods alone. By leveraging to analyze behavioral patterns and predict threat trajectories, AI systems enhance accuracy in forecasting escalations, such as propagation, while integrating with SIEM for proactive alerts. This advancement addresses limitations in manual processes, though it requires robust to mitigate AI-specific risks like adversarial attacks on models.

References

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