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  1. Skraaning and Jamieson raise some interesting issues related to the response of humans to automation failures and offer a taxonomy of failure types that broadens its definition. In this commentary a further attempt to broaden the scope of automation failures is made that places failures within a sociotechnical system of multiple humans and multiple machine components including automation. A suggestion of how one might understand the system’s response to automation failures is offered and the inclusion of autonomy is raised as another complication.

     
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    Free, publicly-accessible full text available January 23, 2025
  2. Resilient teams overcome sudden, dynamic changes by enacting rapid, adaptive responses that maintain system effectiveness. We analyzed two experiments on human-autonomy teams (HATs) operating a simulated remotely piloted aircraft system (RPAS) and correlated dynamical measures of resilience with measures of team performance. Across both experiments, HATs experienced automation and autonomy failures, using a Wizard of Oz paradigm. Team performance was measured in multiple ways, using a mission-level performance score, a target processing efficiency score, a failure overcome score, and a ground truth resilience score. Novel dynamical systems metrics of resilience measured the timing of system reorganization in response to failures across RPAS layers, including vehicle, controls, communications layers, and the system overall. Time to achieve extreme values of reorganization and novelty of reorganization were consistently correlated with target processing efficiency and ground truth resilience across both studies. Correlations with mission-level performance and the overcome score were apparent but less consistent. Across both studies, teams displayed greater system reorganization during failures compared to routine task conditions. The second experiment revealed differential effects of team training focused on coordination coaching and trust calibration. These results inform the measurement and training of resilience in HATs using objective, real-time resilience analysis.

     
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    Free, publicly-accessible full text available December 1, 2024
  3. Objective

    This study examines low-, medium-, and high-performing Human-Autonomy Teams’ (HATs’) communication strategies during various technological failures that impact routine communication strategies to adapt to the task environment.

    Background

    Teams must adapt their communication strategies during dynamic tasks, where more successful teams make more substantial adaptations. Adaptations in communication strategies may explain how successful HATs overcome technological failures. Further, technological failures of variable severity may alter communication strategies of HATs at different performance levels in their attempts to overcome each failure.

    Method

    HATs in a Remotely Piloted Aircraft System-Synthetic Task Environment (RPAS-STE), involving three team members, were tasked with photographing targets. Each triad had two randomly assigned participants in navigator and photographer roles, teaming with an experimenter who simulated an AI pilot in a Wizard of Oz paradigm. Teams encountered two different technological failures, automation and autonomy, where autonomy failures were more challenging to overcome.

    Results

    High-performing HATs calibrated their communication strategy to the complexity of the different failures better than medium- and low-performing teams. Further, HATs adjusted their communication strategies over time. Finally, only the most severe failures required teams to increase the efficiency of their communication.

    Conclusion

    HAT effectiveness under degraded conditions depends on the type of communication strategies enacted by the team. Previous findings from studies of all-human teams apply here; however, novel results suggest information requests are particularly important to HAT success during failures.

    Application

    Understanding the communication strategies of HATs under degraded conditions can inform training protocols to help HATs overcome failures.

     
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    Free, publicly-accessible full text available January 9, 2025
  4. Team workload is a team-level construct considered similar to, but not reducible to, individual workload and mediated by team coordination. Despite this, the conceptualization and measurement of team workload in action teams lags behind that of individual workload. In most empirical studies, team workload is often simply considered as the sum or average of individual team members’ workload. However, unique characteristics of action teams, such as interdependence and heterogeneity, suggest that traditional approaches to conceptualizing and measuring team workload may be inadequate or even misleading. As such, innovative approaches are required to accurately capture this complex construct. This paper presents the development of a simulation designed to investigate the influence of interdependence and demand levels on team workload measures within a 3-person action-team command and control scenario. Preliminary results, which suggest that our manipulations are effective, are provided and discussed.

     
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    Free, publicly-accessible full text available September 1, 2024
  5. Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos’ workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller’s control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at Link. 
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    Free, publicly-accessible full text available August 1, 2024
  6. This expert panel is the first of a two-panel series marking the 40thanniversary of “Cognitive Systems Engineering: New Wine in New Bottles” by Hollnagel and Woods (1983) and, arguably, the beginning of Cognitive Systems Engineering (CSE). These experts were there at (or near) the beginning, devising new methods, expanding and creating new theories, and revealing a new perspective on how complex systems sustain performance and fail. They also wrestled and struggled with these new ideas to propose and implement solutions to improve performance in a number of high-consequence industries. Whether in graduate school or as early-career professionals, they saw the surprises that served as signals that the thinking that brought us to that point would not, alone, be the thinking and doing that would take us further. They will each answer the question, “What ideas and perspectives are important about Cognitive Systems Engineering, and why?”

     
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    Free, publicly-accessible full text available September 1, 2024
  7. Trust plays a critical role in the success of human-robot teams (HRTs). While typically studied as a perceptual attitude, trust also encompasses individual dispositions and interactive behaviors like compliance. Anthropomorphism, the attribution of human-like qualities to robots, is a related phenomenon that designers often leverage to positively influence trust. However, the relationship of anthropomorphism to perceptual, dispositional, and behavioral trust is not fully understood. This study explores how anthropomorphism moderates these relationships in a virtual urban search and rescue HRT scenario. Our findings indicate that the moderating effects of anthropomorphism depend on how a robot’s recommendations and its confidence in them are communicated through text and graphical information. These results highlight the complexity of the relationships between anthropomorphism, trust, and the social conveyance of information in designing for safe and effective human-robot teaming.

     
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    Free, publicly-accessible full text available September 1, 2024
  8. Objective We review the current state-of-the-art in team cognition research, but more importantly describe the limitations of existing theories, laboratory paradigms, and measures considering the increasing complexities of modern teams and the study of team cognition. Background Research on, and applications of, team cognition has led to theories, data, and measures over the last several decades. Method This article is based on research questions generated in a spring 2022 seminar on team cognition at Arizona State University led by the first author. Results Future research directions are proposed for extending the conceptualization of teams and team cognition by examining dimensions of teamness; extending laboratory paradigms to attain more realistic teaming, including nonhuman teammates; and advancing measures of team cognition in a direction such that data can be collected unobtrusively, in real time, and automatically. Conclusion The future of team cognition is one of the new discoveries, new research paradigms, and new measures. Application Extending the concepts of teams and team cognition can also extend the potential applications of these concepts. 
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