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Researchers have long known that general aviation (GA) aircraft exhibit some of the most intense and potentially damaging sound environments to a pilot's hearing. Yet, another potentially more ominous result of this noise-intense environment is the masking of the radio communications. Radio communications must remain intelligible, as they are imperative to the safe and efficient functioning of the airspace, especially the airspace surrounding our busiest airports, Class B and Class C. However, the high amplitude, low frequency noise dominating the GA cockpit causes an upward spreading of masking with such inference that it renders radio communications almost totally unintelligible, unless the pilot is wearing a communications headset. Even with a headset, some researchers have stated that the noise and masking effects overcome the headset performance and still threaten the pilot's hearing and overall safety while in the aircraft. In reaction to this situation, this experiment sought to investigate the effects which active noise reduction (ANR) headsets have on the permissible exposure levels (PELs), speech intelligibility, workload, and ultimately the pilot's performance inside the cockpit. Eight instrument-rated pilot participants flew through different flight tasks of varying levels and types of workload embedded in four 3.5 hour flight scenarios while wearing four different headsets. The 3.5 hours were considered long duration due the instrument conditions, severe weather conditions, difficult flight tasks, and the fatiguing effects of a high intensity noise environment. The noise intensity and spectrum in the simulator facility were specifically calibrated to mimic those of a Cessna 172. Speech intelligibility of radio communications was modified using the Speech Transmission Index (STI), while measures of flight performance and workload were collected to examine any relationships between workload, speech intelligibility, performance, and type of headset. It is believed that the low frequency attenuation advantages afforded by the ANR headset decreased the signal-to-noise ratio, thereby increasing speech intelligibility for the pilot. This increase may positively affect workload and flight performance. Estimates of subjective preference and comfort were also collected and analyzed for relevant relationships. The results of the experiment supported the above hypotheses. It was found that headsets which incorporate ANR technology do increase speech intelligibility which has a direct inverse influence on workload. For example, an increase in speech intelligibility is seen with a concomitant decrease in pilot workload across all types and levels of workload. Furthermore, flight task performance results show that the pilot's headset can facilitate safer flight performance. However, the factors that influence performance are more numerous and complex than those that affect speech intelligibility or workload. Factors such as the operational performance of the communications system in the headset, in addition to the ANR technology, were determined to be highly influential factors in pilot performance. This study has concluded that the pilot's headset has received much research and design attention as a noise attenuation device. However, it has been almost completely overlooked as a tool which could be used to facilitate the safety and performance of a general aviation flight. More research should focus on identifying and optimizing the headset components which contribute most to the results demonstrated in this experiment. The pilot's headset is a component of the aviation system which could economically improve the safety of the entire system. / Ph. D.

Modern complex safety-critical socio-technical systems (STSs) operate in an environment that requires high levels of human-machine interaction. Given the potential for catastrophic events , understanding human errors is a critical research area spanning disciplines such as management science, cognitive engineering, resilience engineering, and systems theory. However, a research gap remains when researching how errors impact system performance from a systemic perspective. This dissertation employs a systematic methodology and develops models that explore the relationship between errors and system performance, considering both macro-organizational and micro-worker perspectives. In Essay 1, the focus is on how firms respond to serious errors (catastrophic events), by exploring the oscillation behavior associated with the organizational learning and forgetting theory. The proposed simulation model contributes to the organizational science literature with a comprehensive approach that assesses the firm's response time to "serious" errors when the firm has a focus on safety with established safety thresholds. All of these considerations have subsequent impact on future performance. Essay 2 explores the relationship between safety-critical system's workers' workload, human error, and automation reliance for the Belgian railway traffic control center. Key findings include a positive relationship between traffic controller performance and workload, and an inverted U-shaped relationship with automation usage. This research offers new insights into the effects of cognitive workload and automation reliance in safety-critical STSs. Essay 3 introduces a calibrated System Dynamics model, informed by empirical data and existing theories on workload suboptimality. This essay contributes to the managerial understanding of workload management, particularly the feedback mechanism between operators' workload and human errors, which is driven by overload and underload thresholds. The model serves as a practical tool for managerial practitioners to estimate the likelihood of human errors based on workload distributions. Overall, this dissertation presents an interdisciplinary and pragmatic approach, blending theoretical and empirical methodologies. Its broad impacts extend across management science, cognitive engineering, and resilience engineering, contributing significantly to the understanding and management of safety-critical socio-technical systems. / Doctor of Philosophy / This dissertation is motivated by the increasing autonomy in infrastructure systems designed to enhance safety performance. Yet paradoxically, we continue to witness system failures leading to catastrophic disasters. High-profile incidents such as the Metro-North train derailment in New York City, the Boeing 737 MAX plane crashes, and the Challenger and Columbia space shuttle accidents highlight this contradiction. This research delves into safety-critical systems where the intricate collaboration between humans and machines is crucial, and where even minor human errors can lead to disastrous consequences. This dissertation is presented in three parts. In the first part I examine how firms react to serious errors. The study focuses on their learning processes following safety incidents and the potential for these lessons to be forgotten over time. I introduced a simulation model grounded in the organizational science literature, offering deeper insights into how companies respond to errors, including changes in safety focus, safety culture, and policy, and the impact of these factors on future company's performance. The second part shifts to a worker-centered perspective, exploring the relationship between workload, performance, and automation usage among traffic controllers. The findings indicate that while performance can improve with an increase in workload up to a certain threshold, excessive reliance on automation may lead to a decline in performance. This part of the study sheds light on how cognitive workload and technology usage influence operators in safety-critical roles. The final part of the dissertation presents another simulation model, this time focusing on how workload, and the resulting stress and boredom due to workload, influence the likelihood of errors. Utilizing real operational data from the Belgian railway transportation system, this model aids managers in understanding how to optimally balance workloads to minimize error risks. Overall, this dissertation takes an interdisciplinary and pragmatic approach, merging theoretical concepts with empirical data. Its extensive impact spans management science, cognitive engineering, and resilience engineering, significantly enhancing our comprehension and management of safety-critical socio-technical systems.

<p>Robotic-assisted surgery (RAS) is one of the most significant advancements in surgical techniques in the past three decades. It provides benefits of reduced infection risks and shortened recovery time over open surgery as well as improved dexterity, stereoscopic vision, and ergonomic console over laparoscopic surgery. The prevalence of RAS systems has increased over years and is expected to grow even larger. However, the major concerns of RAS are the technical difficulty and the system complexity, which can result in long learning time and impose extra cognitive workload and stress on the operating room. Human Factor and Ergonomics (HFE) perspective is critical to patient safety and relevant researches have long provided methods to improve surgical outcomes. Yet, limited studies especially using objective measurements, have been done in the RAS environment. </p> <p> </p> <p>With advances in wearable sensing technology and data analytics, the applications of physiological measures in HFE have been ever increasing. Physiological measures are objective and real-time, free of some main limitations in subjective measures. Eye tracker as a minimally-intrusive and continuous measuring device can provide both physiological and behavioral metrics. These metrics have been found sensitive to changes in workload in various domains. Meanwhile, electroencephalography (EEG) signals capture electrical activity in the cerebral cortex and can reflect cognitive processes that are difficult to assess with other objective measures. Both techniques have the potential to help address some of the challenges in RAS.</p> <p> </p> <p>In this study, eight RAS trainees participated in a 3-month long experiment. In total, they completed 26 robotic skills simulation sessions. In each session, participants performed up to 12 simulated RAS exercises with varying levels of difficulty. For Research Question I, correlation and mixed effect analyses were conducted to explore the relationships between eye tracking metrics and workload. Machine learning classifiers were used to determine the sensitivity of differentiating low and high workload with eye tracking metrics. For Research Question II, two eye tracking metrics and one EEG metric were used to explain participants’ performance changes between consecutive sessions. Correlation and ANOVA analyses were conducted to examine whether variations in performance had significant relationships with variations in objective metrics. Classification models were built to examine the capability of objective metrics in predicting improvement during RAS training. </p> <p> </p> <p>In Research Question I, pupil diameter and gaze entropy distinguished between different task difficulty levels, and both metrics increased as the level of difficulty increased. Yet only gaze entropy was correlated with subjective workload measurement. The classification model achieved an average accuracy of 89.3% in predicting workload levels. In Research Question II, variations in gaze entropy and engagement index were negatively correlated with variations in task performance. Both metrics tended to decrease when performance increased. The classification model achieved an average accuracy of 68.5% in predicting improvements.</p> <p> </p> <p>Eye tracking metrics can measure both task workload and perceived workload during simulated RAS training. It can potentially be used for real-time monitoring of workload in RAS procedure to identify task contributors to high workload and provide insights for training. When combined with EEG, the objective metrics can explain the performance changes during RAS training, and help estimate room for improvements.</p>

<p>Battletank crew must perform many diverse tasks during a normal mission: Crewmembers have to navigate, communicate, control on-board systems, and engage with the enemy, to mention a few. As human processing capacity is limited, the crewmembers will find themselves in situations where task requirements, due to the number of tasks and task complexity, exceed their mental capacity. The stress that results from mental overload has documented quantitative and qualitative effects on performance; effects that could lead to mission failure. </p><p>This thesis describes a simulation of tankcrew during a mission where mental workload is a key factor to the outcome of mission performance. The thesis work has given rise to a number of results. First, conceptual models have been developed of the tank crewmembers. Mental workload is represented in these models as a behavior moderator, which can be manipulated to demonstrate and predict behavioral effects. Second, cognitive models of the tank crewmembers are implemented as Soar agents, which interact with tanks in a 3D simulated battlefield. The empirical data underlying these models was collected from experiments with tankcrew, and involved first hand observations and task analyses. Afterwards, the model’s behavior was verified against an a priori established behavioral pattern and successfully face validated with two subject matter experts.</p>

In this thesis, situations were analyzed in which drivers operate infotainment systems (IVIS) while driving. In this, the focus lay on such situations in which drivers operated these secondary tasks successfully. Following that, a resource orientated approach was chosen in contrast to the focus of many other studies. Demonstrating the negative effects of secondary tasks while driving was less central in this thesis. Rather, everyday behavior adaptations were analyzed that enabled drivers to operate secondary tasks successfully while driving. Therefore these adaptations were measured with regards to the following three factors: driving task, secondary task and mental workload. Additionally the influence of several secondary task attributes was analyzed. Thereby especially the perceived interruptibility was researched in detail. The thesis contains 3 different parts: 1. Introduction to research field, 2. Empiric part and 3. Overall discussion. In the first part an introduction and an overview of the current research concerning secondary task operation while driving is presented. The second part contains 3 studies, each presented in manuscript form. The goal of the first study was to show basic behavior adaptations in a driving simulator study that enables drivers to operate secondary tasks while driving. Thereby it became obvious that drivers adapted their driving behavior as well as their activity in the secondary task dynamically to the specific situation. The driving task was prioritized thereby. The adaptations were dependent on the current as well as the anticipated development of the situations and correspondingly sensitive to the variation of a cue to a hazardous driving situation. If drivers were warned (and thereby an anticipation was possible), they reduced especially their activity in the secondary task. In the second study the influence of mental workload and the attributes of a secondary task were analyzed in-depth. Drivers were informed by a noise signal either about an upcoming unknown driving situation or about an upcoming speed reduction situation in this study. It could be shown that if a secondary task can be interrupted without a perceived decline in performance, it is interrupted in demanding driving situations. If an interruption causes a perceived performance loss, the task is interrupted less often, and so the workload is increased (measured with a physiological measurement). Thus, drivers compensate their current demands by behavior adaptations in different factors, depending on the characteristics of a secondary task. The interaction between driving task, secondary task and workload could be proven by this research. Only if a secondary task could be interrupted without a perceived loss of performance drivers interrupted the task before a hazardous situation was reached. In line with the findings from the studies above a setting was developed for the third study that is less bound to the simulation of complex driving situations and thereby independent from specific driving simulator settings. Nevertheless the anticipation of further driving situations and the option to adapt behavior was given to the drivers by the setting to measure the effects described above. Additionally secondary tasks were analyzed that have a high comparability to common IVIS. Thus, a focus was on the influence of tasks that require time critical inputs. As expected, in tasks with time critical inputs the activity was less often reduced, even if a demanding driving situation was announced. Thereby another influencing factor to the perceived interruptibility of secondary tasks could be analyzed. In the presented studies it was shown that drivers anticipate the further development of a situation and adapt their activity in the secondary task dynamically due to several characteristics of this task. For the future evaluation of IVIS, methodological requirements were deduced from the presented studies and a possible setting for further research was discussed. / In dieser Dissertation werden Situationen untersucht, in denen Fahrer während der Fahrt Infotainmentsysteme (In- Vehicle Infotainment Systeme, kurz IVIS) bedienen. Hierbei wird der Fokus auf Situationen gelegt, in denen Fahrer erfolgreich Nebenaufgaben bearbeiten. Im Gegensatz zu einer Vielzahl von anderen Studien wird hier ein ressourcenorientierter Ansatz gewählt. Im Mittelpunkt steht demnach weniger der Nachweis von Leistungseinbußen in der Fahraufgabe durch zusätzliche Aufgaben. Es wird im Gegensatz dazu herausgearbeitet, durch welche alltäglichen Verhaltensanpassungen Fahrer in der Lage sind, Aufgaben zusätzlich zur Fahraufgabe erfolgreich zu bearbeiten. Dazu werden diese Verhaltensanpassungen messbar gemacht. Ein Hauptaugenmerk wird dabei auf die Faktoren Fahraufgabe, Nebenaufgabe und die mentale Beanspruchung gelegt. Des Weiteren wird der Einfluss verschiedener Nebenaufgaben auf das Verhalten analysiert. Dabei wird insbesondere die wahrgenommene Unterbrechbarkeit der Nebenaufgaben detailliert untersucht. Die Arbeit besteht aus 3 Teilen: 1. Hintergrund des Forschungsfeldes, 2.Experimentalteil und 3. zusammenfassende Diskussion. Im ersten Teil der Arbeit wird zunächst eine Einführung in das Forschungsfeld gegeben und anschließend ein Überblick über den aktuellen Forschungsstand in Bezug auf Zweitaufgabenbearbeitung während der Fahrt. Im Experimentalteil werden 3 Studien präsentiert, die im Rahmen dieser Arbeit durchgeführt wurden (jeweils in Form einer Veröffentlichung). In der ersten Studie war das Ziel grundlegende Verhaltensanpassungen in einer Fahrsimulationsstudie nachzuweisen, die es Fahrern ermöglichen Nebenaufgaben erfolgreich während der Fahrt zu bearbeiten. Dabei wurde deutlich, dass Fahrer ihr Fahrverhalten und ihre Eingabeaktivität in einer Nebenaufgabe der jeweiligen Situation dynamisch anpassen. Die Fahraufgabe wurde dabei priorisiert. Die Verhaltensanpassungen waren sowohl abhängig von der aktuellen, als auch von der antizipierten Situation und zeigten sich demnach abhängig von der Variation eines Hinweisreizes auf eine kritische Verkehrssituation. Als die Fahrer vor einer möglichen Gefahr gewarnt wurden (sie diese also antizipieren konnten), wurde insbesondere die Aktivität in der Nebenaufgabe reduziert. In der daran anschließenden Studie wurde die Rolle der Beanspruchung im Zusammenhang mit den Eigenschaften der Nebenaufgabe näher untersucht. Probanden wurden mithilfe eines Tons entweder auf eine bevorstehende, unbekannte Fahrsituation oder auf eine bevorstehende Geschwindigkeitsreduktion hingewiesen. Es konnte gezeigt werden, dass Fahrer in Situationen, in denen sie den weiteren Fahrverlauf antizipieren und die Nebenaufgabe ohne wahrgenommenen Leistungsverlust unterbrechen konnten, signifikant weniger bedienten. Im Gegensatz dazu zeigte sich in Nebenaufgaben, deren Unterbrechung einen direkten Leistungsverlust nach sich zog, dass Fahrer auch in kritischen Situation gleich viel bedienten. Dieses Verhalten wurde durch eine höhere Anstrengung kompensiert (gemessen mit einem physiologischen Beanspruchungsmaß). Der Zusammenhang der drei Faktoren Fahraufgabe, Nebenaufgabe und Beanspruchung wurde hierbei deutlich. Des Weiteren konnte der Einfluss der Eigenschaften der Nebenaufgaben deutlich gemacht werden: Nur wenn die Unterbrechung der Nebenaufgabe keinen direkten Leistungsverlust zur Folge hatte, wurde diese bereits vor dem Auftreten einer kritischen Situation unterbrochen. Basierend auf den Ergebnissen der ersten beiden Studien wurde für die dritte Studie ein vereinfachtes Setting entwickelt, das weniger auf der Simulation komplexer Fahrsituationen basiert, mithilfe dessen jedoch dennoch die relevanten Effekte messbar sein sollen. Dadurch wird das Setting unabhängiger von einer bestimmten Simulationsumgebung. Dabei wurde den Probanden sowohl ermöglicht relevante Fahrsituationen zu antizipieren als auch ihr Verhalten daran anzupassen. Des Weiteren wurden Nebenaufgaben analysiert, die ähnliche Bedieneingaben erforderten wie gebräuchliche IVIS, und die zum Teil zeitkritische Eingaben erforderten. Wenn Eingaben zeitkritisch gemacht werden mussten, wurde die Nebenaufgabe erwartungsgemäß seltener unterbrochen, auch wenn eine kritische Fahrsituation angekündigt wurde. Dadurch wurde ein weiterer Einflussfaktor auf die wahrgenommene Unterbrechbarkeit von Aufgaben in Fahrsituationen untersucht. In den vorliegenden Studien konnte gezeigt werden, dass Fahrer den weiteren Verlauf von Fahrsituationen antizipieren und ihre Aktivität in einer Nebenaufgabe dynamisch und in Abhängigkeit zu bestimmten Eigenschaften der Nebenaufgabe anpassen. Für die zukünftige Bewertung von IVIS wurden dabei relevante methodische Rahmenbedingungen herausgearbeitet und ein mögliches Setting vorgestellt.

This study aims to test the effects of different way finding affordances on the usability of METU virtual campus built in Second Life virtual world in terms of users&rsquo / satisfaction, performance and mental workload. This study was conducted with 36 participants who are students at the Middle East Technical University. Participants were randomly put on of the five groups namely map, signboard, voice and agent groups and a control group. The participants were given navigational tasks in METU virtual campus within Second Life. In the experiment, the eye movements of the participants were examined with eye tracking tool in order to determine the areas which participants paid attention most. Also mental work load in their prefrontal cortex was examined with fNIR device. There was no significant difference among groups in terms of satisfaction, however / there were significant difference among groups in terms of task completion accuracy, time, length, navigation cue gaze duration and mental workload. Agent group has the highest accuracy score, map group has the highest time, length and navigation cue gaze duration score. Moreover, agent group has the highest mental workload but control group has the lowest mental workload. The results of the tests were used to provide guidance for the design of way finding affordances in METU virtual campus.

Battletank crew must perform many diverse tasks during a normal mission: Crewmembers have to navigate, communicate, control on-board systems, and engage with the enemy, to mention a few. As human processing capacity is limited, the crewmembers will find themselves in situations where task requirements, due to the number of tasks and task complexity, exceed their mental capacity. The stress that results from mental overload has documented quantitative and qualitative effects on performance; effects that could lead to mission failure. This thesis describes a simulation of tankcrew during a mission where mental workload is a key factor to the outcome of mission performance. The thesis work has given rise to a number of results. First, conceptual models have been developed of the tank crewmembers. Mental workload is represented in these models as a behavior moderator, which can be manipulated to demonstrate and predict behavioral effects. Second, cognitive models of the tank crewmembers are implemented as Soar agents, which interact with tanks in a 3D simulated battlefield. The empirical data underlying these models was collected from experiments with tankcrew, and involved first hand observations and task analyses. Afterwards, the model’s behavior was verified against an a priori established behavioral pattern and successfully face validated with two subject matter experts.