Applying the Model of Expert ATC Decision-Making to Analyze a Loss of Separation Incident

Applying the Model of Expert ATC Decision-Making to Analyze a Loss of Separation Incident | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Applying the Model of Expert ATC Decision-Making to Analyze a Loss of Separation Incident Donald Gyles, Chris Bearman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6965001/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In this paper, we describe a new model of expertise-based decision-making in ATC proposed by Gyles and Bearman ( 2025 ) and use this model to analyze a loss of separation incident. The model describes an ongoing iterative process of action-oriented decision-making to ensure that control of the Air Traffic Control (ATC) system is maintained. In the model, ATC decision-making is based on selecting goals, plans, and actions, informed by orientation and situation awareness-building processes, and regulated by a combination of intuitive, automatic responses and deliberate, conscious processes. The model highlighted several issues that enhanced the original analysis of safety factors. These include: 1) the importance of the initial orientation phase to ensure that appropriate goals, plans, and actions are prepared; 2) the need for timely application of conscious, deliberate regulatory processes when relying on automatic intuitive processes; 3) the need to utilize feedforward to anticipate future system states; 4) the requirement to develop implementation intentions to establish sentinel events that can trigger a deliberate conscious review, ensuring that actions and plans still achieve the intended goal; and 5) the need to effectively transition the primary goal from efficiency to safety during loss of separation events. This analysis offers a more nuanced understanding of the reasons behind the controller's actions and provides several new insights into the loss of separation incident. Therefore, the model demonstrates potential as a framework for expertise-based decision-making in ATC and as a tool to enhance the quality of incident investigations, ultimately driving improvements in safety performance. ‘The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality.’ (Simon, 1997 , p. 92) teamwork goals plans adaptive control work as done safety expertise Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Air traffic control (ATC) is currently confronted by the dual challenges of not only meeting increasing demand for services (Eurocontrol, 2022 ; Moon et al., 2011 ) but also maintaining and improving safety performance (Hilburn et al., 2006 ; Kirwan. et al., 2017). While traffic levels continue to grow, safety performance has remained relatively static over the last 15 years (e.g., Airservices Australia, 2020 ; Barry, 2020 ; Huerta et al., 2023 ). Despite ongoing efforts to improve the ATC system's capacity and safety performance via increasing levels of automation, human decision-making is anticipated to continue to take primacy over systems for the foreseeable future (SESAR Joint Undertaking, 2020 ). The effectiveness of ATC relies on the expert performance of individual air traffic controllers (controllers) working collaboratively within a complex socio-technical system (Di Nocera et al., 2006 ; Edwards, 2013 ; Edwards et al., 2016 ; Radišić et al., 2020 ). ATC provides a somewhat unique environment that is highly dynamic, time-constrained, and predominantly managed via a range of cognitive activities (Kontogiannis & Malakis, 2013 ). Cognitive processes and functions, such as perception, attention, memory, language, planning, and decision-making, underpin skilled performance in comprehending the situation, selecting control mechanisms, and communicating with others (Isaac et al., 2002 ; Klein & Wright, 2016 ; Mosleh et al., 2012 ). While controllers generally perform to high standards, their performance is not flawless, and errors still occur (Shorrock & Kirwan, 2002 ). Consequently, most operational incidents attributed to controllers stem from human error (Isaac et al., 2002 ). For example, a study conducted by the Australian Transport Safety Bureau (ATSB) (2013) identified that approximately half of all occurrences resulting in a loss of separation involved contributory actions in the form of errors from ATC. While there has been a significant focus on errors over the last 40 years, ATC errors are still occurring and are leading to incidents and accidents (ATSB, 2013; Dismukes, 2012 ). Meeting the challenge of reducing ATC errors requires that we find innovative ways of understanding and improving performance, and we argue that understanding and improving controller decision-making and the role of expertise (Fox et al., 2013 ) is central to this endeavor (Holbrook et al., 2019 ; Kontogiannis & Malakis, 2017 ; Schroeder et al., 2006 ). In this paper, we discuss a recent model of expert decision-making proposed by Gyles and Bearman ( 2025 ) and demonstrate how it can be utilized to enhance our understanding of ATC errors in investigation reports into loss-of-separation incidents. In particular, we compare the findings of an original incident investigation report with our analysis using Gyles and Bearman’s ( 2025 ) model to 1) demonstrate that a deeper understanding of ATC error can be developed when using this model and 2) propose practical improvements that promise genuine improvement in human performance. In terms of the model, we can 3) show how the model can account for real-world ATC data and 4) provide further explanation of aspects of the model. 2. A model of expertise-based decision-making in ATC An alternative model that can be used to examine human performance and error in ATC has recently been proposed by Gyles and Bearman ( 2025 ) (see Fig. 1 ). This model is grounded primarily in action theory (Frese & Zapf, 1994 ), but also integrates elements of action regulation theory (Jones, 2007 ; Raabe et al., 2007 ), situation awareness theory (Endsley, 2015 ), recognition primed decision-making theory (Klein, 2008 ) and its more recent evolution into macrocognitive theory (Klein & Wright, 2016 ; Mosleh et al., 2012 ; Patterson, 2018 ) and information processing theory (Oppenheimer & Kelso, 2015 ). While the model is well grounded in cognitive theory, it is also tied to workplace-based behavior and is intended for use in the organizational and operational environments of ATC. A brief description of the model is provided below. For more information about the model, including its rationale, development, and detailed description, see Gyles and Bearman ( 2025 ). Gyles and Bearman’s model describes an ongoing iterative process of action-oriented decision-making (Frese & Zapf, 1994 ; Zacher, 2017 ) used to ensure that control of the ATC system is maintained. At the heart of the process are goals and plans that structure plausible action sets. These goals and plans are derived from the job roles and tasks that people perform, as well as the current situation model they have developed. Each plan typically contains actions (or sets of actions) that are carried out to execute the plan when an appropriate opportunity arises. Each step in the process is regulated by a combination of intuitive automatic processes and more effortful, deliberate, analytical processes (Gyles and Bearman ( 2025 ). Throughout the process, feedback is obtained to monitor progress towards the goal. Each of the model's components is outlined in more detail below. 2.1 Orientation and situation awareness (sensemaking) In the orientation process, the controller’s job role and related tasks (Frese & Zapf, 1994 ) indicates sets of cues in the environment that are relevant to the task. This results in a broad, contextualized situation awareness, expressed as a situation model. This serves as a prime in memory for familiar goals, plans, and actions, which are correlated through familiarity and experience with the task and environmental cues (Zacher et al., 2016 ). The orientation process can be enacted consciously, allowing the controller to interpret and redefine the task at hand in a deliberate manner, particularly in novel or complex situations. Alternatively, they may process environmental cues intuitively and automatically based on prior experience (Dane & Pratt, 2007 ; Hacker, 2003 ). 2.2 Goals Goals are cognitive structures that anticipate the future and guide the cognitive work process (Hacker, 1986 ). Goals, in their simplest form, direct our behavior deliberatively toward something we desire in the future. (Brandstätter & Hennecke, 2018 ; Gyles & Bearman, 2025 ). In ATC, multiple goals must be met (efficiency, safety, orderliness, noise abatement, workload management, etc. (Hilburn, 2004 ; Malakis et al., 2010 ), with one of these goals serving as the driver of behavior (the primary goal) and the rest acting as constraints that must be met (Eilon, 1972 ; Simon, 1964 ). For example, in normal circumstances, efficiency (e.g., processing an arrival in the most efficient manner possible) is often the primary goal, so a plan needs to be chosen that is maximally efficient while simultaneously meeting the constraints of safety (e.g., maintaining separation standards), procedural compliance (e.g., control area protection, noise abatement), and workload management (e.g., not exceeding the workload limit). 2.3 Plans Plans are a kind of bridge between goals and actions (Zacher, 2017 ) and consist of sets of behavioral sequences necessary to achieve a goal (Cropanzano et al., 1995 ; Gyles & Bearman, 2025 ). Plans can be specified at varying levels of detail. Experts prefer to work at the highest level of abstraction available to them, so plans can often be nothing more than a list of sub-plans (Zacher et al., 2016 ). Plans can also be very detailed and can include contingency plans to deal with potential or familiar events (Napolitano & Freund, 2016 ). Plans can either be regulated consciously, which involves a deliberate process that uses working memory, or they can be regulated intuitively and automatically via pattern recognition based on the typicality of the situation (Zacher et al., 2016 ). 2.4 Actions (Plan Execution) Plans are executed by performing actions. Some plans include implementation intentions, which are sets of environmental triggers that signal when a plan should be executed (Gollwitzer & Schaal, 1998 ). For well-practiced routines, the execution can be automated when the environmental trigger is encountered (Zacher & Frese, 2018 ). A deliberate implementation intention must be created for unfamiliar or less well-practiced routines. Using environmental cues to trigger plan execution automatically enables decision-makers to use their limited cognitive resources more effectively, allowing them to execute multiple actions simultaneously. However, the downside of this process is that the plans being executed will then largely dictate the perception of cues in the environment (Endsley, 2015 ); therefore, decision-makers are less aware of cues that indicate actions will not achieve the plan (H. Heckhausen & Gollwitzer, 1987 ). Implementation intentions can also be used to identify sentinel events, i.e., critical cues in the environment that can trigger deliberative analytical processes (Gollwitzer & Schaal, 1998 ; Leroux, 1997 ). This allows decision-makers to interrupt well-learned routines so they do not respond automatically or habitually or can trigger a conscious, deliberate check to ensure that actions will achieve the plan (Gollwitzer, 1999 ). With practice and repetition, establishing sentinel events and monitoring progress can become integrated into routines, minimizing the initial cognitive resources required to define them. 2.5 Feedback and Feedforward Feedback provides information about the extent to which an action achieves a plan and the associated goal (Zacher, 2017 ). Feedback typically places demands on the operator's limited cognitive resources. When these resources are used for other activities, such as planning, the feedback process becomes less efficient, making it easy to overlook cues indicating that actions are not aligning with the plan. Feed-forward creates expectations about the system's future states and then compares these expectations to the actual states of the system. (Zacher, 2017 ). Anomalies between expected and actual system states will prompt a deliberate, conscious review. The utilization of feedback and feed-forward mechanisms enables the monitoring of progress in actions, plans, and goal attainment (Reason, 1984 ), which is a crucial aspect of the controller's ongoing decision-making and system control process. 2.6 Regulatory processes Each stage of the decision-making process — including orientation, goals, plans, and actions —is regulated by a combination of intuitive and analytical processes working together. (Brehmer, 1992 ; De Neys, 2023 ; Frese & Zapf, 1994 ; Hacker, 2003 ; Zacher et al., 2016 ). Intuitive processes typically rely on emotions (gut feelings), pattern matching, and implicit knowledge. They are based on instinct, experience, expertise, and subconscious processing of information (Okoli et al., 2016 ; Stanovich & West, 2000 ). Intuitive processing is fast, automatic and largely unconscious (Klein et al., 1989 ). In contrast, analytical processes are the deliberate, conscious processes used to think and reason about our world. These processes are typically relatively slow and cognitively resource-intensive (Patterson et al., 2013 ; Stanovich & West, 2000 ). The intuitive system runs all the time and is frequently the sole basis for decision-making (Zaltman, 2003 ). Goals, plans, and actions are associated with a measure of uncertainty (or feeling of rightness) about the extent to which they will achieve their objective (Ackerman & Thompson, 2017 ; De Neys, 2023 ). When uncertainty is low, we navigate the decision-making process without engaging in analytical thinking. However, if uncertainty surpasses a certain threshold, conscious analytical processes will be activated until uncertainty is reduced or a commitment to a decision must be made. (De Neys, 2023 ). 3. Using the model to analyze a case study of an ATC safety occurrence In this section, we analyze an ATC Incident where safety was compromised using the Gyles and Bearman ( 2025 ) model of expertise-based decision-making in ATC (Fig. 1 ). The case study was selected from a dataset of 27 official investigation reports of controller-attributed incidents by a civilian Air Navigation Service Provider (ANSP) that occurred within a single calendar year. This study’s research protocol was approved and overseen by the Central Queensland University Human Research Ethics Committee, approval number H15/07–16. The original reports did not contain any personal information relating to the staff involved, and additional steps were taken to further de-identify and anonymize the reported data to further protect the identities of those involved. The incident occurred at an aerodrome when the radar separation between an arriving Airbus A320 and the following Boeing B789 on the Instrument Landing System (ILS) approach reduced below the minimum 3 nautical mile (nm) radar standard. Both aircraft in this occurrence (shown in bold in Figs. 2 , 3 , & 4 ) landed on a southerly facing runway, and the B789 was inbound on the final approach path from approximately 50nm to run. The A320 was inbound from the Southeast and joined the final approach path via a track perpendicular to the final approach path (Fig. 2 ). The A320 was under the control of Approach Controller #1, and the B789 was under the control of Approach Controller #2. Both aircraft became established on the extended runway centerline and were subject to the same wind effect when the A320 joined at 10 nm final, 4 minutes before touchdown (see Fig. 3 ). The Flow Controller 1 planned for the two aircraft to land approximately 2 and a half minutes apart, which equates to a nominal 5nm spacing as the first aircraft touches down. B789 was approximately 1 minute early when they first called Approach Controller #2 at approximately 50 nm to run to the runway threshold. The A320 was running late by about 30 seconds when they contacted Approach Controller #1. This would equate to a spacing of 2.5nm with the preceding A320 as it touched down if no further action was taken. The controller managing the following aircraft is responsible for achieving appropriate spacing with the preceding aircraft. The two principal means of delaying an aircraft once it is within the approach controller's airspace are speed control (i.e., slowing down the aircraft) or radar vectoring to increase the distance flown to the landing threshold. Speed control can achieve minor adjustments, and more significant delaying action can typically be achieved using radar vectoring. In this situation, the controller of the lead aircraft A320 (Controller #1), canceled the STAR speed restrictions (CSSR in radar label) to achieve the initially planned landing time. The controller of the following aircraft (Controller #2) chose to use speed restrictions, issuing progressive speed reduction instructions to the pilot of B789 to increase spacing with the preceding aircraft. However, this proved ineffective, with the closing speed between the two aircraft from when B789 passed 20nm to run until B789 was asked to break off the approach and go-around varying between 30 and 60 knots (kts). Spacing first reduced below 4nm, which required coordination with the Aerodrome controller, but this was not done. Approach Controller #2 then tried to establish visual separation 2 via the Aerodrome Controller, and when this was unsuccessful, by asking the pilot of B789 if they could sight and follow the preceding A320. Neither of these strategies was successful, as B789 was in cloud. As separation reduced to 3nm (Fig. 4 ), the B789 was issued a go-around instruction, and separation reduced to 2.4nm as they accelerated before the vertical separation of 1000’ was re-established. This means the two aircraft operated without the required safety margin, i.e., a loss of separation while in cloud and closing. Using the model of expertise-based decision-making in ATC (Fig. 1 ), we can gain further insights into why this incident occurred, what went wrong, and how to prevent similar incidents in the future. The following sections provide a detailed analysis, and Table 2 summarizes our findings, including the model's components and our recommendations for improvement. Appendix A (online resource) – Decision Element Interaction Mapping Framework, outlines the methodology and tools used to analyze the original occurrent report through the lens of the 10 elements in the model of expertise based ATC decision making. This includes temporal mapping of 4 critical phases of the occurrence and the presence or absence of the model of expert decision-making in ATC’s (Fig. 1 ) key elements in each phase as well as consideration of key element interactions or dyads. This analysis then forms the basis of the explanatory narrative outlined in this paper. This occurrence had its origin in the orientation and situation awareness process, which led to the selection of a plan that was unlikely to meet the goal and subsequent reliance on intuitive automatic processing without a check to ensure the plan was working. The effectiveness of intuitive automatic control largely depends on ‘typicality’ (Klein & Hoffman, 1993 ) or the extent to which the current situation matches and continues to match the person’s previous experiences and associated solutions (i.e., goals, plans, and actions) held in long-term memory. The orientation process and accurate situational awareness are, therefore, centrally important. It appears that the significance of the B789 being on straight in final from 50nm out (minimum time for speed control to have an effect), the likely lack of effectiveness of canceling the A320 STAR speed restrictions, and the relatively low cloud base on the day (which invalidated the usual contingency plans) were not fully appreciated in this regard. This meant that the selected plan (speed control) and contingency plans (tower visual separation or pilot visual separation) were not well-matched to the circumstances and were unlikely to meet the goal. Controller #2 had just started their shift when the incident occurred, completing the handover 5 seconds before B789 first called on the frequency. This suggests that the handover and self-briefing process was inadequate in orienting the controller to the air traffic situation. They also had limited exposure to recent, contextually relevant experiences to supplement the orientation process. The use of intuitive automatic processes to manage the cognitive work process was achieved without employing feedforward to anticipate future system states or establishing an implementation intention that included a sentinel event to consciously and deliberately verify that the plan was viable and on track. This meant the plan was enacted with little conscious attention until it failed. In this incident, a potential sentinel event would have occurred when B789 reached 20 nautical miles from the runway. At that stage, A320 should have been approximately 2 minutes flying time ahead of B789 (at their then-current speed). A failure to achieve this spacing should have initiated replanning and radar vectoring to dogleg B789, thereby increasing the distance flown to the runway and losing the required time to achieve the required spacing with A320. Instead, intuitive automatic control continued until it became clear that the original plan would not achieve the intended spacing and that the two contingency plans could not be implemented. It was then recognized that the safety constraint (3nm separation standard) was about to be compromised, ultimately leading to the instruction for the B789 to go around. Once the imminent loss of separation was recognized, there was a failure to change the primary goal in the management of B789 from 'efficiency’ to 'safety’ in a timely manner. The change in goal was only done once separation was about to be infringed, and it became apparent via feedback that the plan to meet the goal of ‘efficiency’ would no longer meet the constraint of ‘safety.’ The switch to the safety goal resulted in issuing a go-around instruction to B789. It is unlikely that the issuance of the go-around instruction would be elicited as part of the intuitive automatic processes because controllers do not frequently issue go-around instructions during routine work. Therefore, a switch to conscious deliberate control was required. It was, however, possible to develop an implementation intention based on a sentinel event ahead of time that could have triggered a switch. For example, “If B789 is not at or below a ground speed of 190 at 10nm, send them around.” Forming an implementation intention transfers control of task execution to a specified anticipated environmental cue, which can help to reduce the working memory load and protect against distraction (Gyles & Bearman, 2025 ). 4. Discussion In this paper, we analyzed a loss of separation incident using Gyles and Bearman’s ( 2025 ) model of expertise-based decision-making in ATC to 1) demonstrate that a deeper understanding of the factors influencing ATC performance can be developed when using this model and 2) propose practical improvements that promise genuine improvement in human performance. In the process, we also 3) explored whether the model can account for real-world ATC data and 4) further explained aspects of the model. 4.1 Factors influencing human performance The model of expertise-based decision-making in ATC (Gyles & Bearman, 2025 ) is grounded in an extensive literature review on human performance and decision-making. As such, it provides a more sophisticated account of ATC performance and decision-making than the safety factors identified in the report. This model's insights enable a more nuanced exploration of what happened and, more importantly, why it occurred, in terms of the underlying cognitive work and regulatory processes. This analysis emphasizes the importance of: 1) the initial orientation phase so that appropriate goals, plans, and actions are primed. 2) appropriate use of conscious, deliberate regulatory processes if there is an initial reliance on automatic, intuitive processes. 3) the need to use feedforward to anticipate future system states. 4) the need to use implementation intentions to set sentinel events that can trigger a deliberate conscious review to ensure that actions and plans will still achieve the goal. 5) the need to efficiently transition the primary goal from efficiency to safety in loss of separation events. 4.2 Practical improvements based on insights from the model The analysis showed the importance of understanding the limitations of intuitive automatic control. Most of the time, the controller’s goals, plans, and actions will be regulated using the intuitive automatic regulatory system to use limited cognitive resources most effectively. The effectiveness of intuitive automatic control mainly depends on ‘typicality’ (Klein & Hoffman, 1993 ) or the extent to which the current situation matches the person’s previous experiences and associated solutions (goal/plan). So, provided the situation being experienced is typical of situations previously observed, this control method works well, and we can perform numerous tasks simultaneously. However, if the situation is not typical, it can lead to errors. One of the functions of the handover and self-briefing process at the start of a shift is to identify anomalies that may invalidate typicality (i.e., something not done by the controller in this incident). Given our reliance on intuitive processing in everyday work, it is vital to anticipate future system states by using feedforward and implementation intentions to set sentinel events, ensuring that actions and plans will achieve the desired goals. If the orientation process leads to plans and actions that will not achieve the goal (as in this incident), this can be detected by the checking process and rectified. Without this checking process, the plans and actions will continue with little conscious thought, essentially making this an ‘all or nothing bet’ that the situation is typical and that the plans and actions will work. Such checking mechanisms are an important way for experts to trap errors and are part of the ongoing process of expert decision-making and system control in ATC (Helmreich & Merritt, 2000 ; Kontogiannis & Malakis, 2009 ). How ATCs respond to emergency or nonnormal situations, such as compromised separation scenarios, has been a key safety focus for some time (Malakis et al., 2010 ). The issue of slow or inadequate response has generally been approached from the perspective of a technical skill deficiency arising from a lack of practice in using the required phraseologies, failing to convey a sense of urgency, and, where relevant, failing to use large enough avoidance turns in a radar surveillance environment. Simulation or computer-based part-task simulations have been deployed as part of refresher training to cultivate a more automatic response to compromised separation situations. Based on the model of expertise-based decision-making in ATC, we can see that implementing an effective compromised separation response involves more than just technical skills (Kirwan. et al., 2017). It also involves complex cognitive strategies, specifically a goal transition, usually from efficiency to safety. Safety critical tasks that require goal transitions (e.g., from efficiency to safety) and replanning (initiating a go-around) are low-frequency events for controllers, so they are unlikely to be carried out using the fast, intuitive automatic regulation system. Instead, they will generally require relatively slow, conscious, deliberate processes under conditions that may be time-pressured and stressful (e.g., in a loss of separation). Automatic regulation in such situations is possible, but an implementation intention must have primed it. Once the intention is formed, it can be executed automatically in response to a sentinel event in the environment. Thus, system recovery training for controllers could be supplemented using training in the creation of implementation intentions. 4.3 Reflections on the model of expertise-based decision-making in ATC The model used in this paper identified a range of underlying cognitive factors that led to the loss of separation in the occurrence report. This analysis led to a more nuanced understanding of why controllers made their decisions, yielding several novel insights into the causes of the loss of separation. As such, the model shows promise as a framework for decision-making in ATC and as a means to enhance the quality of incident investigations, thereby facilitating further improvements in safety performance. While we do not advocate replacing the current activities underlying the occurrence investigation process, we advocate supplementing it with this new model. With this new focus on controller performance as the central element of the air traffic control system, it may be possible to surpass the current level of realized safety performance. 4.4 Limitations Several limitations of this study should be noted. The case study used in this paper's review is based on secondary data sources. As such, we cannot guarantee the extent to which it reflects a comprehensive record of everything that transpired in the occurrence. Occurrence reports are written and edited for a specific purpose, generally to support the findings and conclusions drawn from them. There may have been aspects of the occurrence relevant to our new model that have not been included in the report used. An additional limitation is that the occurrence report has been largely interrogated by a single researcher (the first author), which may lead to inaccuracy or bias in the analysis. In part, the researcher’s 40 years of experience in air traffic control and human factors expertise should minimize the likelihood of misunderstandings and inaccuracies in the reporting, although bias could still occur. To minimize this, we have presented as much raw data, including transcripts and screenshots, as possible to assist readers in assessing the reliability and validity of our conclusions. 5. Conclusions The opening quote in this paper, by Simon ( 1997 ), aims to illustrate the significant challenge faced by decision-makers in complex environments, where decisions are expected to be based on objective rationality. Simon ( 1997 ) noted that this level of rationality is often unattainable for humans, except in the simplest situations. Consequently, decision-makers, such as air traffic controllers, must engage in a delicate balancing act, dynamically and iteratively combining intuitive and deliberative processes. This enables them to consider just enough information to make quick decisions while minimizing cognitive resource expenditure to avoid overload and maintain system control. Ultimately, the effectiveness and relevance of decisions made and actions taken then depend on the strategies employed and the breadth of information considered (Gigerenzer & Gaissmaier, 2011 ; Grawitch et al., 2025 ; Grawitz, 2024 ). Decision-makers are rationally bounded by the circumstances they find themselves in and are inevitably forced to satisfice, seeking good-enough decisions (Simon, 1957 ), which they may then be compelled to justify using a rationality they did not initially employ. The expertise-based human performance model outlined in this paper, along with its application to the case study, aims to rectify this anomaly and has demonstrated itself to be an invaluable instrument for examining the vulnerabilities inherent in human performance within the context of air traffic control. The model focuses on goals, plans, and actions, which are informed by an orienting process and controlled by a regulatory system that utilizes automatic, intuitive, and deliberative analytical control processes to achieve ecological rationality (Gigerenzer & Todd, 1999 ). The model, therefore, reflects a human factors theory that is both descriptive of the work of controllers and specified in detailed cognitive terms. As such, it potentially enables investigators to integrate behavioral-based observations with underlying cognitions and consideration of human factors, making a unique and innovative contribution to the current literature. For practitioners, the model shows great promise as a means to improve the quality of occurrence investigations, identify trends and patterns, and inform local and strategic interventions, thereby leveraging further improvement in safety performance. Declarations Declaration of Interests: Neither author has any declaration of interests to declare. Author Contribution Both D.G. and C.B. contributed to the conception, drafting and revision of this manuscript. D.G. prepared all figures and tables.Both D.G. and C.B. read and approved the final manuscript. Acknowledgement This paper represents the authors' interpretations and perspectives alone. It does not necessarily reflect the official position of the organization that granted access to the occurrence reports comprising our research dataset. The authors wish to thank Matthew Thomas for his helpful comments on the early draft of this manuscript. References Ackerman, R., & Thompson, V. A. (2017). Meta-Reasoning: Monitoring and Control of Thinking and Reasoning. Trends in Cognitive Sciences , 21 (8), 607–617. https://doi.org/10.1016/j.tics.2017.05.004 Airservices Australia. (2020). 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Applied Ergonomics , 33 (4), 319–336. https://doi.org/10.1016/S0003-6870(02)00010-8 Simon, H. A. (1957). Models of man; social and rational (pp. xiv, 287). Wiley. Simon, H. A. (1964). On the Concept of Organizational Goal. Administrative Science Quarterly , 9 (1), 1–22. https://doi.org/10.2307/2391519 Simon, H. A. (1997). Administrative Behavior, 4th Edition . Simon and Schuster. Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate? Open Peer Commentary-Are there two different types of thinking? Behavioral and Brain Sciences , 23 (5), 645–726. Zacher, H. (2017). Action Regulation Theory. In Oxford research encyclopedia of psychology. https://doi.org/10.1093/acrefore/9780190236557.013.1 Zacher, H., & Frese, M. (2018). Action Regulation Theory: Foundations, Current Knowledge, and Future Directions. In D. Ones, H. Sinangil, C. Viswesvaran, & N. Anderson (Eds.), The SAGE handbook of industrial, work and organizational psychology (2nd ed., pp. 122–143). Zacher, H., Hacker, W., & Frese, M. (2016). Action regulation across the adult lifespan (ARAL): A metatheory of work and aging. Work, Aging and Retirement , 2 (3), 286–306. https://academic.oup.com/workar/article-abstract/2/3/286/1753546 Zaltman, G. (2003). How Customers Think: Essential Insights Into the Mind of the Market . Harvard Business School Press. Footnotes The controller that manages flow control, implementing measures to regulate traffic entering specific airspace, following designated routes, or heading to particular aerodromes, ensuring optimal use of airspace or aerodromes. (Airservices Australia and Department of Defence, 2024 ). A means of spacing aircraft through the use of visual observation by a tower controller or by a pilot when assigned separation responsibility (Airservices Australia and Department of Defence, 2024 ). Standard Terminal Area Arrival Speeds [replaced now by published STAR speeds] (Airservices Australia, 2024 ) A controller-issued time for an aircraft to overfly a fix or position typically 50nm from the landing threshold designed to achieve a specific landing time. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Nov, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 25 Jun, 2025 Submission checks completed at journal 25 Jun, 2025 First submitted to journal 24 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6965001","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490096861,"identity":"49d13086-68a3-4d95-b799-d3142dd4e2b2","order_by":0,"name":"Donald Gyles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDCCA0D8gIFBDsJjI1ZLAgODMelaEhuI1sJ3gP2aREJFXXp/e44Bw4eywwz8MxLwa5E8wFMmkXDmcO6MM28MGGecO8wgcYOAFoMDPGkSiW0HcjdI5Bgw87YdZmAgTsu/unQDkJa/QC3yhLWwH5NIbGBOAGthBGoxIKRF8jAPs0XCscOGM848KzjYcy6dx/DMA/xa+I63P7zxoaZOnr89eeODH2XWcnLHCdjCwMxjIgFhJYDjiIeAehBgf/wBpmUUjIJRMApGAVYAADJtRp4zb3FEAAAAAElFTkSuQmCC","orcid":"","institution":"Central Queensland University","correspondingAuthor":true,"prefix":"","firstName":"Donald","middleName":"","lastName":"Gyles","suffix":""},{"id":490096862,"identity":"34d825cf-0527-4d40-9025-7f8b9b66af8a","order_by":1,"name":"Chris Bearman","email":"","orcid":"","institution":"Central Queensland University","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Bearman","suffix":""}],"badges":[],"createdAt":"2025-06-24 10:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6965001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6965001/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87642333,"identity":"de4a49ea-5959-4cb1-a828-0a39ee3faab7","added_by":"auto","created_at":"2025-07-26 15:21:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32029,"visible":true,"origin":"","legend":"\u003cp\u003eModel of Expert Decision-Making in Air Traffic Control (© Gyles and Bearman, 2025, reproduced with permission)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6965001/v1/7c35d4f81f5f35577eaa6904.png"},{"id":87642334,"identity":"73a95dfe-da8c-4ded-bab4-40cac9f40f69","added_by":"auto","created_at":"2025-07-26 15:21:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11911,"visible":true,"origin":"","legend":"\u003cp\u003ePositions of A320 and B789 on their tracks to final approach.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6965001/v1/e9c59d4a602ec0d1056ee68c.png"},{"id":87642335,"identity":"53f0bb24-d84a-4b2b-92bf-74176f9cf3c0","added_by":"auto","created_at":"2025-07-26 15:21:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11684,"visible":true,"origin":"","legend":"\u003cp\u003ePosition of A320 and B789 at 10nm final with 5nm spacing\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6965001/v1/12c5d7b3011a3d54abc8c5a9.png"},{"id":87642576,"identity":"782b319a-7998-4aa3-a8f7-334c1591b13e","added_by":"auto","created_at":"2025-07-26 15:29:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10123,"visible":true,"origin":"","legend":"\u003cp\u003ePosition of A320 and B789 on 3nm final with 2.9nm spacing, indicating a loss of separation.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6965001/v1/ec809cce72efd180415f5b55.png"},{"id":87642921,"identity":"c2ed0fdc-1d8d-4b5f-9547-095eb122a0e9","added_by":"auto","created_at":"2025-07-26 15:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":836396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6965001/v1/59f99a32-1146-4d43-96f7-491430ae509b.pdf"},{"id":87642336,"identity":"c1bc5360-1c03-4d26-8f76-fdf1d9655f5d","added_by":"auto","created_at":"2025-07-26 15:21:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22268,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-6965001/v1/5bf6e524071a1e6d4d2802b8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applying the Model of Expert ATC Decision-Making to Analyze a Loss of Separation Incident","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAir traffic control (ATC) is currently confronted by the dual challenges of not only meeting increasing demand for services (Eurocontrol, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) but also maintaining and improving safety performance (Hilburn et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kirwan. et al., 2017). While traffic levels continue to grow, safety performance has remained relatively static over the last 15 years (e.g., Airservices Australia, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Barry, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huerta et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite ongoing efforts to improve the ATC system's capacity and safety performance via increasing levels of automation, human decision-making is anticipated to continue to take primacy over systems for the foreseeable future (SESAR Joint Undertaking, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe effectiveness of ATC relies on the expert performance of individual air traffic controllers (controllers) working collaboratively within a complex socio-technical system (Di Nocera et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Edwards, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Edwards et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Radišić et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). ATC provides a somewhat unique environment that is highly dynamic, time-constrained, and predominantly managed via a range of cognitive activities (Kontogiannis \u0026amp; Malakis, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Cognitive processes and functions, such as perception, attention, memory, language, planning, and decision-making, underpin skilled performance in comprehending the situation, selecting control mechanisms, and communicating with others (Isaac et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Klein \u0026amp; Wright, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mosleh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile controllers generally perform to high standards, their performance is not flawless, and errors still occur (Shorrock \u0026amp; Kirwan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Consequently, most operational incidents attributed to controllers stem from human error (Isaac et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For example, a study conducted by the Australian Transport Safety Bureau (ATSB) (2013) identified that approximately half of all occurrences resulting in a loss of separation involved contributory actions in the form of errors from ATC. While there has been a significant focus on errors over the last 40 years, ATC errors are still occurring and are leading to incidents and accidents (ATSB, 2013; Dismukes, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Meeting the challenge of reducing ATC errors requires that we find innovative ways of understanding and improving performance, and we argue that understanding and improving controller decision-making and the role of expertise (Fox et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) is central to this endeavor (Holbrook et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kontogiannis \u0026amp; Malakis, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schroeder et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this paper, we discuss a recent model of expert decision-making proposed by Gyles and Bearman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and demonstrate how it can be utilized to enhance our understanding of ATC errors in investigation reports into loss-of-separation incidents. In particular, we compare the findings of an original incident investigation report with our analysis using Gyles and Bearman\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) model to 1) demonstrate that a deeper understanding of ATC error can be developed when using this model and 2) propose practical improvements that promise genuine improvement in human performance. In terms of the model, we can 3) show how the model can account for real-world ATC data and 4) provide further explanation of aspects of the model.\u003c/p\u003e"},{"header":"2. A model of expertise-based decision-making in ATC","content":"\u003cp\u003eAn alternative model that can be used to examine human performance and error in ATC has recently been proposed by Gyles and Bearman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This model is grounded primarily in action theory (Frese \u0026amp; Zapf, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), but also integrates elements of action regulation theory (Jones, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Raabe et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), situation awareness theory (Endsley, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), recognition primed decision-making theory (Klein, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and its more recent evolution into macrocognitive theory (Klein \u0026amp; Wright, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mosleh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Patterson, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and information processing theory (Oppenheimer \u0026amp; Kelso, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While the model is well grounded in cognitive theory, it is also tied to workplace-based behavior and is intended for use in the organizational and operational environments of ATC. A brief description of the model is provided below. For more information about the model, including its rationale, development, and detailed description, see Gyles and Bearman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGyles and Bearman\u0026rsquo;s model describes an ongoing iterative process of action-oriented decision-making (Frese \u0026amp; Zapf, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Zacher, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used to ensure that control of the ATC system is maintained. At the heart of the process are goals and plans that structure plausible action sets. These goals and plans are derived from the job roles and tasks that people perform, as well as the current situation model they have developed. Each plan typically contains actions (or sets of actions) that are carried out to execute the plan when an appropriate opportunity arises. Each step in the process is regulated by a combination of intuitive automatic processes and more effortful, deliberate, analytical processes (Gyles and Bearman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Throughout the process, feedback is obtained to monitor progress towards the goal. Each of the model's components is outlined in more detail below.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Orientation and situation awareness (sensemaking)\u003c/h2\u003e\u003cp\u003eIn the orientation process, the controller\u0026rsquo;s job role and related tasks (Frese \u0026amp; Zapf, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) indicates sets of cues in the environment that are relevant to the task. This results in a broad, contextualized situation awareness, expressed as a situation model. This serves as a prime in memory for familiar goals, plans, and actions, which are correlated through familiarity and experience with the task and environmental cues (Zacher et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The orientation process can be enacted consciously, allowing the controller to interpret and redefine the task at hand in a deliberate manner, particularly in novel or complex situations. Alternatively, they may process environmental cues intuitively and automatically based on prior experience (Dane \u0026amp; Pratt, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hacker, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Goals\u003c/h2\u003e\u003cp\u003eGoals are cognitive structures that anticipate the future and guide the cognitive work process (Hacker, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Goals, in their simplest form, direct our behavior deliberatively toward something we desire in the future. (Brandst\u0026auml;tter \u0026amp; Hennecke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gyles \u0026amp; Bearman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In ATC, multiple goals must be met (efficiency, safety, orderliness, noise abatement, workload management, etc. (Hilburn, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Malakis et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), with one of these goals serving as the driver of behavior (the primary goal) and the rest acting as constraints that must be met (Eilon, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Simon, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1964\u003c/span\u003e). For example, in normal circumstances, efficiency (e.g., processing an arrival in the most efficient manner possible) is often the primary goal, so a plan needs to be chosen that is maximally efficient while simultaneously meeting the constraints of safety (e.g., maintaining separation standards), procedural compliance (e.g., control area protection, noise abatement), and workload management (e.g., not exceeding the workload limit).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Plans\u003c/h2\u003e\u003cp\u003ePlans are a kind of bridge between goals and actions (Zacher, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and consist of sets of behavioral sequences necessary to achieve a goal (Cropanzano et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Gyles \u0026amp; Bearman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Plans can be specified at varying levels of detail. Experts prefer to work at the highest level of abstraction available to them, so plans can often be nothing more than a list of sub-plans (Zacher et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Plans can also be very detailed and can include contingency plans to deal with potential or familiar events (Napolitano \u0026amp; Freund, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Plans can either be regulated consciously, which involves a deliberate process that uses working memory, or they can be regulated intuitively and automatically via pattern recognition based on the typicality of the situation (Zacher et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Actions (Plan Execution)\u003c/h2\u003e\u003cp\u003ePlans are executed by performing actions. Some plans include implementation intentions, which are sets of environmental triggers that signal when a plan should be executed (Gollwitzer \u0026amp; Schaal, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For well-practiced routines, the execution can be automated when the environmental trigger is encountered (Zacher \u0026amp; Frese, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A deliberate implementation intention must be created for unfamiliar or less well-practiced routines. Using environmental cues to trigger plan execution automatically enables decision-makers to use their limited cognitive resources more effectively, allowing them to execute multiple actions simultaneously. However, the downside of this process is that the plans being executed will then largely dictate the perception of cues in the environment (Endsley, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); therefore, decision-makers are less aware of cues that indicate actions will not achieve the plan (H. Heckhausen \u0026amp; Gollwitzer, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImplementation intentions can also be used to identify sentinel events, i.e., critical cues in the environment that can trigger deliberative analytical processes (Gollwitzer \u0026amp; Schaal, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Leroux, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This allows decision-makers to interrupt well-learned routines so they do not respond automatically or habitually or can trigger a conscious, deliberate check to ensure that actions will achieve the plan (Gollwitzer, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). With practice and repetition, establishing sentinel events and monitoring progress can become integrated into routines, minimizing the initial cognitive resources required to define them.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Feedback and Feedforward\u003c/h2\u003e\u003cp\u003eFeedback provides information about the extent to which an action achieves a plan and the associated goal (Zacher, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Feedback typically places demands on the operator's limited cognitive resources. When these resources are used for other activities, such as planning, the feedback process becomes less efficient, making it easy to overlook cues indicating that actions are not aligning with the plan. Feed-forward creates expectations about the system's future states and then compares these expectations to the actual states of the system. (Zacher, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Anomalies between expected and actual system states will prompt a deliberate, conscious review. The utilization of feedback and feed-forward mechanisms enables the monitoring of progress in actions, plans, and goal attainment (Reason, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), which is a crucial aspect of the controller's ongoing decision-making and system control process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Regulatory processes\u003c/h2\u003e\u003cp\u003eEach stage of the decision-making process \u0026mdash; including orientation, goals, plans, and actions \u0026mdash;is regulated by a combination of intuitive and analytical processes working together. (Brehmer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; De Neys, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Frese \u0026amp; Zapf, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Hacker, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zacher et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Intuitive processes typically rely on emotions (gut feelings), pattern matching, and implicit knowledge. They are based on instinct, experience, expertise, and subconscious processing of information (Okoli et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stanovich \u0026amp; West, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Intuitive processing is fast, automatic and largely unconscious (Klein et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). In contrast, analytical processes are the deliberate, conscious processes used to think and reason about our world. These processes are typically relatively slow and cognitively resource-intensive (Patterson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stanovich \u0026amp; West, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The intuitive system runs all the time and is frequently the sole basis for decision-making (Zaltman, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Goals, plans, and actions are associated with a measure of uncertainty (or feeling of rightness) about the extent to which they will achieve their objective (Ackerman \u0026amp; Thompson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; De Neys, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When uncertainty is low, we navigate the decision-making process without engaging in analytical thinking. However, if uncertainty surpasses a certain threshold, conscious analytical processes will be activated until uncertainty is reduced or a commitment to a decision must be made. (De Neys, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Using the model to analyze a case study of an ATC safety occurrence","content":"\u003cp\u003eIn this section, we analyze an ATC Incident where safety was compromised using the Gyles and Bearman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) model of expertise-based decision-making in ATC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The case study was selected from a dataset of 27 official investigation reports of controller-attributed incidents by a civilian Air Navigation Service Provider (ANSP) that occurred within a single calendar year. This study\u0026rsquo;s research protocol was approved and overseen by the Central Queensland University Human Research Ethics Committee, approval number H15/07\u0026ndash;16. The original reports did not contain any personal information relating to the staff involved, and additional steps were taken to further de-identify and anonymize the reported data to further protect the identities of those involved.\u003c/p\u003e\u003cp\u003eThe incident occurred at an aerodrome when the radar separation between an arriving Airbus A320 and the following Boeing B789 on the Instrument Landing System (ILS) approach reduced below the minimum 3 nautical mile (nm) radar standard.\u003c/p\u003e\u003cp\u003eBoth aircraft in this occurrence (shown in bold in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) landed on a southerly facing runway, and the B789 was inbound on the final approach path from approximately 50nm to run. The A320 was inbound from the Southeast and joined the final approach path via a track perpendicular to the final approach path (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The A320 was under the control of Approach Controller #1, and the B789 was under the control of Approach Controller #2. Both aircraft became established on the extended runway centerline and were subject to the same wind effect when the A320 joined at 10 nm final, 4 minutes before touchdown (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Flow Controller\u003csup\u003e1\u003c/sup\u003e planned for the two aircraft to land approximately 2 and a half minutes apart, which equates to a nominal 5nm spacing as the first aircraft touches down. B789 was approximately 1 minute early when they first called Approach Controller #2 at approximately 50 nm to run to the runway threshold. The A320 was running late by about 30 seconds when they contacted Approach Controller #1. This would equate to a spacing of 2.5nm with the preceding A320 as it touched down if no further action was taken.\u003c/p\u003e\u003cp\u003eThe controller managing the following aircraft is responsible for achieving appropriate spacing with the preceding aircraft. The two principal means of delaying an aircraft once it is within the approach controller's airspace are speed control (i.e., slowing down the aircraft) or radar vectoring to increase the distance flown to the landing threshold. Speed control can achieve minor adjustments, and more significant delaying action can typically be achieved using radar vectoring. In this situation, the controller of the lead aircraft A320 (Controller #1), canceled the STAR speed restrictions (CSSR in radar label) to achieve the initially planned landing time. The controller of the following aircraft (Controller #2) chose to use speed restrictions, issuing progressive speed reduction instructions to the pilot of B789 to increase spacing with the preceding aircraft. However, this proved ineffective, with the closing speed between the two aircraft from when B789 passed 20nm to run until B789 was asked to break off the approach and go-around varying between 30 and 60 knots (kts).\u003c/p\u003e\u003cp\u003eSpacing first reduced below 4nm, which required coordination with the Aerodrome controller, but this was not done. Approach Controller #2 then tried to establish visual separation\u003csup\u003e2\u003c/sup\u003e via the Aerodrome Controller, and when this was unsuccessful, by asking the pilot of B789 if they could sight and follow the preceding A320. Neither of these strategies was successful, as B789 was in cloud. As separation reduced to 3nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the B789 was issued a go-around instruction, and separation reduced to 2.4nm as they accelerated before the vertical separation of 1000\u0026rsquo; was re-established. This means the two aircraft operated without the required safety margin, i.e., a loss of separation while in cloud and closing.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"584\" height=\"478\"\u003e\u003c/p\u003e\u003cp\u003eUsing the model of expertise-based decision-making in ATC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we can gain further insights into why this incident occurred, what went wrong, and how to prevent similar incidents in the future. The following sections provide a detailed analysis, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes our findings, including the model's components and our recommendations for improvement. Appendix A (online resource) \u0026ndash; Decision Element Interaction Mapping Framework, outlines the methodology and tools used to analyze the original occurrent report through the lens of the 10 elements in the model of expertise based ATC decision making. This includes temporal mapping of 4 critical phases of the occurrence and the presence or absence of the model of expert decision-making in ATC\u0026rsquo;s (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) key elements in each phase as well as consideration of key element interactions or dyads. This analysis then forms the basis of the explanatory narrative outlined in this paper.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"584\" height=\"493\"\u003e\u003c/p\u003e\u003cp\u003eThis occurrence had its origin in the orientation and situation awareness process, which led to the selection of a plan that was unlikely to meet the goal and subsequent reliance on intuitive automatic processing without a check to ensure the plan was working.\u003c/p\u003e\u003cp\u003eThe effectiveness of intuitive automatic control largely depends on \u0026lsquo;typicality\u0026rsquo; (Klein \u0026amp; Hoffman, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) or the extent to which the current situation matches and continues to match the person\u0026rsquo;s previous experiences and associated solutions (i.e., goals, plans, and actions) held in long-term memory. The orientation process and accurate situational awareness are, therefore, centrally important. It appears that the significance of the B789 being on straight in final from 50nm out (minimum time for speed control to have an effect), the likely lack of effectiveness of canceling the A320 STAR speed restrictions, and the relatively low cloud base on the day (which invalidated the usual contingency plans) were not fully appreciated in this regard. This meant that the selected plan (speed control) and contingency plans (tower visual separation or pilot visual separation) were not well-matched to the circumstances and were unlikely to meet the goal. Controller #2 had just started their shift when the incident occurred, completing the handover 5 seconds before B789 first called on the frequency. This suggests that the handover and self-briefing process was inadequate in orienting the controller to the air traffic situation. They also had limited exposure to recent, contextually relevant experiences to supplement the orientation process.\u003c/p\u003e\u003cp\u003eThe use of intuitive automatic processes to manage the cognitive work process was achieved without employing feedforward to anticipate future system states or establishing an implementation intention that included a sentinel event to consciously and deliberately verify that the plan was viable and on track. This meant the plan was enacted with little conscious attention until it failed. In this incident, a potential sentinel event would have occurred when B789 reached 20 nautical miles from the runway. At that stage, A320 should have been approximately 2 minutes flying time ahead of B789 (at their then-current speed). A failure to achieve this spacing should have initiated replanning and radar vectoring to dogleg B789, thereby increasing the distance flown to the runway and losing the required time to achieve the required spacing with A320. Instead, intuitive automatic control continued until it became clear that the original plan would not achieve the intended spacing and that the two contingency plans could not be implemented. It was then recognized that the safety constraint (3nm separation standard) was about to be compromised, ultimately leading to the instruction for the B789 to go around.\u003c/p\u003e\u003cp\u003eOnce the imminent loss of separation was recognized, there was a failure to change the primary goal in the management of B789 from 'efficiency\u0026rsquo; to 'safety\u0026rsquo; in a timely manner. The change in goal was only done once separation was about to be infringed, and it became apparent via feedback that the plan to meet the goal of \u0026lsquo;efficiency\u0026rsquo; would no longer meet the constraint of \u0026lsquo;safety.\u0026rsquo; The switch to the safety goal resulted in issuing a go-around instruction to B789. It is unlikely that the issuance of the go-around instruction would be elicited as part of the intuitive automatic processes because controllers do not frequently issue go-around instructions during routine work. Therefore, a switch to conscious deliberate control was required. It was, however, possible to develop an implementation intention based on a sentinel event ahead of time that could have triggered a switch. For example, \u0026ldquo;If B789 is not at or below a ground speed of 190 at 10nm, send them around.\u0026rdquo; Forming an implementation intention transfers control of task execution to a specified anticipated environmental cue, which can help to reduce the working memory load and protect against distraction (Gyles \u0026amp; Bearman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this paper, we analyzed a loss of separation incident using Gyles and Bearman\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) model of expertise-based decision-making in ATC to 1) demonstrate that a deeper understanding of the factors influencing ATC performance can be developed when using this model and 2) propose practical improvements that promise genuine improvement in human performance. In the process, we also 3) explored whether the model can account for real-world ATC data and 4) further explained aspects of the model.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Factors influencing human performance\u003c/h2\u003e\u003cp\u003eThe model of expertise-based decision-making in ATC (Gyles \u0026amp; Bearman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) is grounded in an extensive literature review on human performance and decision-making. As such, it provides a more sophisticated account of ATC performance and decision-making than the safety factors identified in the report. This model's insights enable a more nuanced exploration of what happened and, more importantly, why it occurred, in terms of the underlying cognitive work and regulatory processes. This analysis emphasizes the importance of:\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e1) the initial orientation phase so that appropriate goals, plans, and actions are primed.\u003c/h3\u003e\n\u003cp\u003e2) appropriate use of conscious, deliberate regulatory processes if there is an initial reliance on automatic, intuitive processes.\u003c/p\u003e\n\u003ch3\u003e3) the need to use feedforward to anticipate future system states.\u003c/h3\u003e\n\u003cp\u003e4) the need to use implementation intentions to set sentinel events that can trigger a deliberate conscious review to ensure that actions and plans will still achieve the goal.\u003c/p\u003e\u003cp\u003e5) the need to efficiently transition the primary goal from efficiency to safety in loss of separation events.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Practical improvements based on insights from the model\u003c/h2\u003e\u003cp\u003eThe analysis showed the importance of understanding the limitations of intuitive automatic control. Most of the time, the controller\u0026rsquo;s goals, plans, and actions will be regulated using the intuitive automatic regulatory system to use limited cognitive resources most effectively. The effectiveness of intuitive automatic control mainly depends on \u0026lsquo;typicality\u0026rsquo; (Klein \u0026amp; Hoffman, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) or the extent to which the current situation matches the person\u0026rsquo;s previous experiences and associated solutions (goal/plan). So, provided the situation being experienced is typical of situations previously observed, this control method works well, and we can perform numerous tasks simultaneously. However, if the situation is not typical, it can lead to errors. One of the functions of the handover and self-briefing process at the start of a shift is to identify anomalies that may invalidate typicality (i.e., something not done by the controller in this incident).\u003c/p\u003e\u003cp\u003eGiven our reliance on intuitive processing in everyday work, it is vital to anticipate future system states by using feedforward and implementation intentions to set sentinel events, ensuring that actions and plans will achieve the desired goals. If the orientation process leads to plans and actions that will not achieve the goal (as in this incident), this can be detected by the checking process and rectified. Without this checking process, the plans and actions will continue with little conscious thought, essentially making this an \u0026lsquo;all or nothing bet\u0026rsquo; that the situation is typical and that the plans and actions will work. Such checking mechanisms are an important way for experts to trap errors and are part of the ongoing process of expert decision-making and system control in ATC (Helmreich \u0026amp; Merritt, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kontogiannis \u0026amp; Malakis, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHow ATCs respond to emergency or nonnormal situations, such as compromised separation scenarios, has been a key safety focus for some time (Malakis et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The issue of slow or inadequate response has generally been approached from the perspective of a technical skill deficiency arising from a lack of practice in using the required phraseologies, failing to convey a sense of urgency, and, where relevant, failing to use large enough avoidance turns in a radar surveillance environment. Simulation or computer-based part-task simulations have been deployed as part of refresher training to cultivate a more automatic response to compromised separation situations. Based on the model of expertise-based decision-making in ATC, we can see that implementing an effective compromised separation response involves more than just technical skills (Kirwan. et al., 2017). It also involves complex cognitive strategies, specifically a goal transition, usually from efficiency to safety. Safety critical tasks that require goal transitions (e.g., from efficiency to safety) and replanning (initiating a go-around) are low-frequency events for controllers, so they are unlikely to be carried out using the fast, intuitive automatic regulation system. Instead, they will generally require relatively slow, conscious, deliberate processes under conditions that may be time-pressured and stressful (e.g., in a loss of separation). Automatic regulation in such situations is possible, but an implementation intention must have primed it. Once the intention is formed, it can be executed automatically in response to a sentinel event in the environment. Thus, system recovery training for controllers could be supplemented using training in the creation of implementation intentions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Reflections on the model of expertise-based decision-making in ATC\u003c/h2\u003e\u003cp\u003eThe model used in this paper identified a range of underlying cognitive factors that led to the loss of separation in the occurrence report. This analysis led to a more nuanced understanding of why controllers made their decisions, yielding several novel insights into the causes of the loss of separation. As such, the model shows promise as a framework for decision-making in ATC and as a means to enhance the quality of incident investigations, thereby facilitating further improvements in safety performance. While we do not advocate replacing the current activities underlying the occurrence investigation process, we advocate supplementing it with this new model. With this new focus on controller performance as the central element of the air traffic control system, it may be possible to surpass the current level of realized safety performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Limitations\u003c/h2\u003e\u003cp\u003eSeveral limitations of this study should be noted. The case study used in this paper's review is based on secondary data sources. As such, we cannot guarantee the extent to which it reflects a comprehensive record of everything that transpired in the occurrence. Occurrence reports are written and edited for a specific purpose, generally to support the findings and conclusions drawn from them. There may have been aspects of the occurrence relevant to our new model that have not been included in the report used. An additional limitation is that the occurrence report has been largely interrogated by a single researcher (the first author), which may lead to inaccuracy or bias in the analysis. In part, the researcher\u0026rsquo;s 40 years of experience in air traffic control and human factors expertise should minimize the likelihood of misunderstandings and inaccuracies in the reporting, although bias could still occur. To minimize this, we have presented as much raw data, including transcripts and screenshots, as possible to assist readers in assessing the reliability and validity of our conclusions.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe opening quote in this paper, by Simon (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), aims to illustrate the significant challenge faced by decision-makers in complex environments, where decisions are expected to be based on objective rationality. Simon (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) noted that this level of rationality is often unattainable for humans, except in the simplest situations. Consequently, decision-makers, such as air traffic controllers, must engage in a delicate balancing act, dynamically and iteratively combining intuitive and deliberative processes. This enables them to consider just enough information to make quick decisions while minimizing cognitive resource expenditure to avoid overload and maintain system control. Ultimately, the effectiveness and relevance of decisions made and actions taken then depend on the strategies employed and the breadth of information considered (Gigerenzer \u0026amp; Gaissmaier, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Grawitch et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Grawitz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Decision-makers are rationally bounded by the circumstances they find themselves in and are inevitably forced to satisfice, seeking good-enough decisions (Simon, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1957\u003c/span\u003e), which they may then be compelled to justify using a rationality they did not initially employ.\u003c/p\u003e\u003cp\u003eThe expertise-based human performance model outlined in this paper, along with its application to the case study, aims to rectify this anomaly and has demonstrated itself to be an invaluable instrument for examining the vulnerabilities inherent in human performance within the context of air traffic control. The model focuses on goals, plans, and actions, which are informed by an orienting process and controlled by a regulatory system that utilizes automatic, intuitive, and deliberative analytical control processes to achieve ecological rationality (Gigerenzer \u0026amp; Todd, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The model, therefore, reflects a human factors theory that is both descriptive of the work of controllers and specified in detailed cognitive terms. As such, it potentially enables investigators to integrate behavioral-based observations with underlying cognitions and consideration of human factors, making a unique and innovative contribution to the current literature. For practitioners, the model shows great promise as a means to improve the quality of occurrence investigations, identify trends and patterns, and inform local and strategic interventions, thereby leveraging further improvement in safety performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Interests:\u003c/h2\u003e\n\u003cp\u003eNeither author has any declaration of interests to declare.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eBoth D.G. and C.B. contributed to the conception, drafting and revision of this manuscript. D.G. prepared all figures and tables.Both D.G. and C.B. read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis paper represents the authors\u0026apos; interpretations and perspectives alone. It does not necessarily reflect the official position of the organization that granted access to the occurrence reports comprising our research dataset. The authors wish to thank Matthew Thomas for his helpful comments on the early draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAckerman, R., \u0026amp; Thompson, V. A. (2017). Meta-Reasoning: Monitoring and Control of Thinking and Reasoning. \u003cem\u003eTrends in Cognitive Sciences\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(8), 607\u0026ndash;617. https://doi.org/10.1016/j.tics.2017.05.004\u003c/li\u003e\n\u003cli\u003eAirservices Australia. (2020). \u003cem\u003eAIR NAVIGATION SERVICES OPERATIONAL SAFETY REPORTING AND PERFORMANCE LONG-TERM TRENDS\u003c/em\u003e. https://www.airservices.gov.au/wp-content/uploads/ANS-Safety-Reporting-and-Performance30Apr2020.pdf\u003c/li\u003e\n\u003cli\u003eAirservices Australia. 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(2006). \u003cem\u003eA Human Factors Review of the Operational Error Literature\u003c/em\u003e (No. DOT/FAA/AM-06/21; p. 68). Civil Aerospace Medical Institute Federal Aviation Administration. https://www.researchgate.net/profile/Julia-Pounds/publication/331895373_A_Human_Factors_Review_of_the_Operational_Error_Literature/links/62980020416ec50bdb0357f3/A-Human-Factors-Review-of-the-Operational-Error-Literature.pdf\u003c/li\u003e\n\u003cli\u003eSESAR Joint Undertaking. (2020). \u003cem\u003eEuropean ATM Master Plan 2020 Exec View.pdf\u003c/em\u003e. https://www.sesarju.eu/sites/default/files/documents/reports/European%20ATM%20Master%20Plan%202020%20Exec%20View.pdf\u003c/li\u003e\n\u003cli\u003eShorrock, S. T., \u0026amp; Kirwan, B. (2002). 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Open Peer Commentary-Are there two different types of thinking? \u003cem\u003eBehavioral and Brain Sciences\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5), 645\u0026ndash;726.\u003c/li\u003e\n\u003cli\u003eZacher, H. (2017). Action Regulation Theory. In \u003cem\u003eOxford research encyclopedia of psychology.\u003c/em\u003e https://doi.org/10.1093/acrefore/9780190236557.013.1\u003c/li\u003e\n\u003cli\u003eZacher, H., \u0026amp; Frese, M. (2018). Action Regulation Theory: Foundations, Current Knowledge, and Future Directions. In D. Ones, H. Sinangil, C. Viswesvaran, \u0026amp; N. Anderson (Eds.), \u003cem\u003eThe SAGE handbook of industrial, work and organizational psychology\u003c/em\u003e (2nd ed., pp. 122\u0026ndash;143).\u003c/li\u003e\n\u003cli\u003eZacher, H., Hacker, W., \u0026amp; Frese, M. (2016). Action regulation across the adult lifespan (ARAL): A metatheory of work and aging. \u003cem\u003eWork, Aging and Retirement\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(3), 286\u0026ndash;306. https://academic.oup.com/workar/article-abstract/2/3/286/1753546\u003c/li\u003e\n\u003cli\u003eZaltman, G. (2003). \u003cem\u003eHow Customers Think: Essential Insights Into the Mind of the Market\u003c/em\u003e. Harvard Business School Press.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The controller that manages flow control, implementing measures to regulate traffic entering specific airspace, following designated routes, or heading to particular aerodromes, ensuring optimal use of airspace or aerodromes. (Airservices Australia and Department of Defence, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e A means of spacing aircraft through the use of visual observation by a tower controller or by a pilot when assigned separation responsibility (Airservices Australia and Department of Defence, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Standard Terminal Area Arrival Speeds [replaced now by published STAR speeds] (Airservices Australia, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e A controller-issued time for an aircraft to overfly a fix or position typically 50nm from the landing threshold designed to achieve a specific landing time.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"teamwork, goals, plans, adaptive control, work as done, safety, expertise","lastPublishedDoi":"10.21203/rs.3.rs-6965001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6965001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we describe a new model of expertise-based decision-making in ATC proposed by Gyles and Bearman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and use this model to analyze a loss of separation incident. The model describes an ongoing iterative process of action-oriented decision-making to ensure that control of the Air Traffic Control (ATC) system is maintained. In the model, ATC decision-making is based on selecting goals, plans, and actions, informed by orientation and situation awareness-building processes, and regulated by a combination of intuitive, automatic responses and deliberate, conscious processes.\u003c/p\u003e\u003cp\u003eThe model highlighted several issues that enhanced the original analysis of safety factors. These include: 1) the importance of the initial orientation phase to ensure that appropriate goals, plans, and actions are prepared; 2) the need for timely application of conscious, deliberate regulatory processes when relying on automatic intuitive processes; 3) the need to utilize feedforward to anticipate future system states; 4) the requirement to develop implementation intentions to establish sentinel events that can trigger a deliberate conscious review, ensuring that actions and plans still achieve the intended goal; and 5) the need to effectively transition the primary goal from efficiency to safety during loss of separation events. This analysis offers a more nuanced understanding of the reasons behind the controller's actions and provides several new insights into the loss of separation incident. Therefore, the model demonstrates potential as a framework for expertise-based decision-making in ATC and as a tool to enhance the quality of incident investigations, ultimately driving improvements in safety performance.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026lsquo;The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality.\u0026rsquo;\u003c/em\u003e (Simon, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, p. 92)\u003c/p\u003e","manuscriptTitle":"Applying the Model of Expert ATC Decision-Making to Analyze a Loss of Separation Incident","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-26 15:21:22","doi":"10.21203/rs.3.rs-6965001/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-09T15:50:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T07:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266403011792402799018550044242166812962","date":"2025-09-11T07:17:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T15:50:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198726525280241449711034648332126625891","date":"2025-07-25T17:58:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266403011792402799018550044242166812962","date":"2025-07-24T05:43:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T18:15:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-25T11:24:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T11:23:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cognition, Technology \u0026 Work","date":"2025-06-24T10:49:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cognition-technology-and-work","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctwo","sideBox":"Learn more about [Cognition, Technology \u0026 Work](http://link.springer.com/journal/10111)","snPcode":"10111","submissionUrl":"https://submission.nature.com/new-submission/10111/3","title":"Cognition, Technology \u0026 Work","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"77c36e20-acf9-45e5-945b-ce548f25c3cc","owner":[],"postedDate":"July 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T18:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-26 15:21:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6965001","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6965001","identity":"rs-6965001","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}