Improving Teamwork Competencies in Human-Machine Teams: Perspectives From Team Science.

In response to calls for research to improve human-machine teaming (HMT), we present a "perspective" paper that explores techniques from computer science that can enhance machine agents for human-machine teams. As part of this paper, we (1) summarize the state of the science on critical team competencies identified for effective HMT, (2) discuss technological gaps preventing machines from fully realizing these competencies, and (3) identify ways that emerging artificial intelligence (AI) capabilities may address these gaps and enhance performance in HMT. We extend beyond extant literature by incorporating recent technologies and techniques and describing their potential for contributing to the advancement of HMT.

Overload and automation-dependence in a multi-UAS simulation: Task demand and individual difference factors.

Future unmanned aerial systems (UAS) operations will require control of multiple vehicles. Operators are vulnerable to cognitive overload, despite support from system automation. This study tested whether attentional resource theory predicts impacts of cognitive demands on performance measures, including automation-dependence and stress. It also investigated individual differences in response to demands. One-hundred and 1 university student participants performed a multi-UAS simulation mission incorporating 2 surveillance tasks. Cognitive demands and level of automation (LOA) of key tasks were manipulated between-subjects. Results were partially consistent with predictions. Higher task demands impaired performance and elevated distress and workload, as expected. Higher LOA produced greater dependence on automation, but failed to mitigate workload. It was expected that, as the automation was quite reliable, participants would attempt to conserve resources by depending more on automation under high demand. In fact, the opposite tendency was observed. Individuals high in conscientiousness were especially likely to override the automation under high demand, apparently taking charge personally. Neuroticism and distress were also associated with performance, but results did not fit a resource theory interpretation. Thus, understanding impacts of overload in the multi-UAS context requires understanding operator strategy as well as resource insufficiency. Findings have implications for system design, and operator selection and training. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Metacognition, Hardiness, and Grit as Resilience Factors in Unmanned Aerial Systems (UAS) Operations: A Simulation Study.

Operators of Unmanned Aerial Systems (UAS) face a variety of stress factors resulting from both the cognitive demands of the work and its broader social context. Dysfunctional metacognitions including those concerning worry may increase stress vulnerability, whereas personality traits including hardiness and grit may confer resilience. The present study utilized a simulation of UAS operation requiring control of multiple vehicles. Two stressors were manipulated independently in a within-subjects design: cognitive demands and negative evaluative feedback. Stress response was assessed using both subjective measures and a suite of psychophysiological sensors, including the electroencephalogram (EEG), electrocardiogram (ECG), and hemodynamic sensors. Both stress manipulations elevated subjective distress and elicited greater high-frequency activity in the EEG. However, predictors of stress response varied across the two stressors. The Anxious Thoughts Inventory (AnTI: Wells, 1994) was generally associated with higher state worry in both control and stressor conditions. It also predicted stress reactivity indexed by EEG and worry responses in the negative feedback condition. Measures of hardiness and grit were associated with somewhat different patterns of stress response. In addition, within the negative feedback condition, the AnTI meta-worry scale moderated relationships between state worry and objective performance and psychophysiological outcome measures. Under high state worry, AnTI meta-worry was associated with lower frontal oxygen saturation, but higher spectral power in high-frequency EEG bands. High meta-worry may block adaptive compensatory effort otherwise associated with worry. Findings support both the metacognitive theory of anxiety and negative emotions (Wells and Matthews, 2015), and the Trait-Stressor-Outcome (TSO: Matthews et al., 2017a) framework for resilience.

Dangerous intersections? A review of studies of fatigue and distraction in the automated vehicle.

The impacts of fatigue on the vehicle driver may change with technological advancements including automation and the increasing prevalence of potentially distracting in-car systems. This article reviews the authors' simulation studies of how fatigue, automation, and distraction may intersect as threats to safety. Distinguishing between states of active and passive fatigue supports understanding of fatigue and the development of countermeasures. Active fatigue is a stress-like state driven by overload of cognitive capabilities. Passive fatigue is produced by underload and monotony, and is associated with loss of task engagement and alertness. Our studies show that automated driving reliably elicits subjective symptoms of passive fatigue and also loss of alertness that persists following manual takeover. Passive fatigue also impairs attention and automation use in operators of Remotely Piloted Vehicles (RPVs). Use of in-vehicle media has been proposed as a countermeasure to fatigue, but such media may also be distracting. Studies tested whether various forms of phone-based media interacted with automation-induced fatigue, but effects were complex and dependent on task configuration. Selection of fatigue countermeasures should be guided by an understanding of the form of fatigue confronting the operator. System design, regulation of level of automation, managing distraction, and selection of fatigue-resilient personnel are all possible interventions for passive fatigue, but careful evaluation of interventions is necessary prior to deployment.

Vigilance and Automation Dependence in Operation of Multiple Unmanned Aerial Systems (UAS): A Simulation Study.

OBJECTIVE: This simulation study investigated factors influencing sustained performance and fatigue during operation of multiple Unmanned Aerial Systems (UAS). The study tested effects of time-on-task and automation reliability on accuracy in surveillance tasks and dependence on automation. It also investigated the role of trait and state individual difference factors. BACKGROUND: Warm's resource model of vigilance has been highly influential in human factors, but further tests of its applicability to complex, real-world tasks requiring sustained attention are necessary. Multi-UAS operation differs from standard vigilance paradigms in that the operator must switch attention between multiple subtasks, with support from automation. METHOD: 131 participants performed surveillance tasks requiring signal discrimination and symbol counting with a multi-UAS simulation configured to impose low cognitive demands, for 2 hr. Automation reliability was manipulated between-groups. Five Factor Model personality traits were measured prior to performance. Subjective states were assessed with the Dundee Stress State Questionnaire. RESULTS: Performance accuracy on the more demanding surveillance task showed a vigilance decrement, especially when automation reliability was low. Dependence on automation on this task declined over time. State but not trait factors predicted performance. High distress was associated with poorer performance in more demanding task conditions. CONCLUSIONS: Vigilance decrement may be an operational issue for multi-UAS surveillance missions. Warm's resource theory may require modification to incorporate changes in information processing and task strategy associated with multitasking in low-workload, fatiguing environments. APPLICATION: Interface design and operator evaluation for multi-UAS operations should address issues including motivation, stress, and sustaining attention to automation.