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Jon Holbrook

Publications and source records attributed to Jon Holbrook.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Psychophysiological Research Methods to Assess Airline Flight Crew Resilient Performance in High-Fidelity Flight Simulation Scenarios

New concepts in aviation system safety thinking have emerged to consider not only what may go wrong, but also what can be learned when things go right. This approach forms a more comprehensive approach to system safety thinking. A need exists for methods to enable a better understanding of human contributions to aviation safety and how they may inform Safety Management Systems (SMS). A high-fidelity 737-800 simulation study was conducted to study how current type-rated commercial airline flight crews anticipate, monitor, respond to, and learn from expected and unexpected disturbances during line operations. A number of dependent measures were collected that included traditional SMS data types, but also non-traditional safety data to include multiple psychophysiological metrics. This paper describes the psychophysiological measures results that evinced the capability of measures to help identify resilient flight crews. Implications for future research and design of future In-time Aviation Safety Management Systems are discussed.

Psychophysiology↗
NASA NTRS2021

Guidance and Lessons Learned from COVID-19 for Human-Subjects Research

The goal of this effort was to identify innovative strategies, approaches, and methods that have enabled researchers to continue doing meaningful human subjects research during the COVID-19 pandemic. Many organizations and recent literature primarily focus on efforts for future research in a post-COVID environment. Additionally, there have been no systematic efforts across the agency or broader human factors community to coordinate or share strategies. Therefore, the focus was on acquiring knowledge gained by researchers from experiences during COVID that could be valuable to continue human-subjects research under existing restrictions.

Coronavirus Disease 2019↗
NASA NTRS2021

Resilience Engineering's Potential for Advanced Air Mobility (AAM)

The national airspace (NAS) will rapidly evolve in the next ten to twenty years. Plans for Advanced Air Mobility (AAM) during that period envision highly automated airspace management systems and electrically powered vehicles. AAM concepts also anticipate limited human roles. The goal of limiting the human role is to minimize the potential for misadventures, yet how the human role is limited needs to be carefully considered in order to also preserve the potential for human successes. The field of resilience engineering (RE) focuses on how systems can change in order to seize an opportunity or withstand an unforeseen challenge. RE methods rely on the use of empirical data to optimize the ability of any system to adapt. RE studies have shown how individual and team initiatives ensure resilient system performance by creating safety through flexibility. Benefits of the RE approach include improved awareness of operational circumstances and how system elements depend on each other, and the ability to allocate limited resources and prepare for surprise. RE offers the ability to account for and incorporate the human role as an essential element in order to ensure NAS systems’ resilient performance. Data on the human contribution to safe and resilient system performance, which is termed “work as done,” are available but are not being considered as the NAS evolves. We present an approach that describes how use of RE can enable the evolving NAS to adapt, and perform, in a resilient manner.

Aviation Safety↗
NASA NTRSDate not supplied

Creating Formal Characterizations ofRoutine Contingency Management inCommercial Aviation

Traditional approaches to safety management focus on collection of data describing unwanted states (i.e., accidents and incidents) and analysis of undesired behaviors (i.e., faults and errors) that precede those states. Thus, in the traditional view of safety, safety is both defined and measured by its absence, namely the lack of safety. In extremely high confidence systems like commercial air transport, however, opportunities to measure the absence of safety are relatively rare. Ironically, a critical barrier to measuring safety and the impact of mitigation strategies in commercial aviation is the lack of opportunities for measurement.

Intelligent Contingency Management,↗
NASA NTRSDate not supplied

A Data-Driven Approach to Recognizing and Understanding Human Contributions to Aviation Safety

Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence, and are influenced by, how decision makers characterize problems and identify solutions. In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on safety-producing behaviors, safety management and system design decisions are based on a small sample of non-representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to aviation safety represents a significant and largely unrecognized risk when making policy decisions about safety management and system design. Opportunities exist to leverage the vast amount of data that have already been collected, or could be easily obtained, to increase our understanding of human contributions to safety in commercial aviation. This presentation will focus on those opportunities as well as the challenges associated with collecting and analyzing data on operators’ safety-producing behaviors.

Safety↗
NASA NTRSDate not supplied

Creating Formal Characterizations of Routine Contingency Management in Commercial Aviation

The identification, modelling, and analysis of root causes of accidents and incidents dominate conventional safety management approaches. However, the effect of humans’ safety-producing behavior on the overall resilience of the system is often neglected. Additionally, emerging aviation markets are giving rise to concepts of operation, such as urban air mobility and optionally piloted air cargo operations, that are leading to a shift in locus of control between humans and automation. Without an understanding of the human contribution to safety, it is difficult to assess the effects of these novel role allocations on overall system safety. In this work, safety-producing behaviors are identified and abstracted into resilient performance strategies. Production rules that encapsulate these strategies are then generated and classified in the Soar cognitive architecture. The strategies are then applied to a remotely-operated air cargo example to demonstrate how safe learning is facilitated. The learned rules and strategies are then formally verified.

Safety Critical Systems↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety-producing behaviors in addition to safety-reducing behaviors. Opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors are discussed.

Aviation Safety↗
NASA NTRSDate not supplied

How Do Different Knowledge Frameworks Help Us Learn From Aviation Line Observations?

Human performance includes actions that increase safety, as well as actions that can reduce safety. Ensuring safety in complex dynamic operations like commercial aviation depends on the ability to institute appropriate responses based on what is learned from flightcrew performance and the contexts in which it occurs. To do this systemically at the organization level requires collecting data on flightcrew performance, developing effective approaches to analyzing those data, and understanding how to translate what has been learned into policies, procedures, and practice. Systematic observation of front-line operators is a vital source of human performance data. Much has been learned from such observations, including methodological principles. Most observations have been based on a framework focused on managing safety challenges and the ensuing unsafe events. A complementary perspective focuses on flexibility and actions that promote continued safe and effective operation. We consider lessons learned about observational methods from an established framework focused on undesired actions and how these might be extended for a framework focused on desired actions.

safety↗
NASA NTRSDate not supplied

Evaluating the Use of High-Fidelity Simulator Research Methods to Study Airline Flight Crew Resilience

As it evolves, aviation will continue to require integration of a wide range of safety systems and practices, some of which are already in place and others that are yet to be developed. New concepts in system safety thinking have emerged to consider not only what may go wrong, but also what can be learned when things go right during commercial flight operations. Taken together, these complementary perspectives form a more comprehensive approach to systemsafety thinking that can help to recognize and preserve the resilient performance capabilities currently provided by humans. A need exists, however, for research methods to enable better understanding of the human contributions to aviation safety. NASA’s System-Wide Safety Project supports research on using flight simulation methods to study operator resilience and safety-producing behaviors. Building on prior NASA efforts investigating procedural non-adherences during area navigation standard terminal route arrivals, a high-fidelity commercial aviation line operational simulation (LOS) experiment has been designed to study how flight crews anticipate, monitor for, respond to, and learn from expected and unexpected disturbances during these operations. A diverse set of LOS scenarios were developed to simulate highly realistic, complex, but routinely encountered operational situations. Each scenario provided multiple opportunities to collect data on how flight crews manage threats and errors, as well as novel opportunities to observe resilient and safety-producing behaviors. The experimental design, implications for the study of safety-producing behaviors using simulation, and considerations for airline pilot training will be discussed.

Chad L Stephens↗
NASA NTRSDate not supplied

Thinking outside the box: The human role in increasingly automated aviation systems

Rapid advances in artificial intelligence are enabling automated systems to operate in an increasingly autonomous manner in domains that previously required the involvement of human operators. Examples are rail transport systems, self-driving cars, and warehouse delivery systems. From time to time, such automation encounters operational conditions that fall outside a “competency box” within which the system has been designed to operate. Human operators add resilience because they can see and act outside the competency box of scenarios and environments for which the system was designed. The system’s competencies can be expanded over time with modifications to software, sensors, etc.; however, it is unclear at what point the competency box becomes large enough to safely eliminate the role of the human operator. One area where advanced automation may be applied is Urban Air Mobility (UAM). Current UAM concepts envision fleets of highly automated air vehicles providing on-demand transport for people and goods. A phased development of UAM has been proposed, beginning with on-board pilots and transitioning to a future state where automated vehicles operate with minimal human involvement. Proponents of UAM note that this final state reduces cost as well as eliminating pilot error, identified as a contributing factor in many aircraft accidents. However, eliminating human involvement also risks eliminating their positive contributions to system resilience. Here we examine Concepts of Operation proposed for future UAM systems and explore how humans can best be incorporated to maintain resilience while minimizing cost and risk. A human-autonomy teaming approach is suggested.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors. The panel will discuss opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors.

aviation safety↗