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Objective: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. Background: Under the prevalence effect, rare signals are more difficult to detect, even when taking into account their proportionally low occurrence. This decline represents diminished human capability to both detect and respond. As signal probability (SP) approaches zero, accuracy exhibits logarithmic decay. Cybersecurity, a context in which the environment is entirely artificial, provides an opportunity to manufacture conditions enhancing or degrading human performance, such as prevalence effects. Email cybersecurity prevalence effects have not previously been demonstrated, nor intentionally manipulated. Method: The Email Testbed (ET) provides a simulation of a clerical email work involving messages containing sensitive personal information. Using the ET, participants were presented with 300 email interactions and received cyberattacks at rates of either 1%, 5%, or 20%. Results: Results demonstrated the existence and power of prevalence effects in email cybersecurity. Attacks delivered at a rate of 1% were significantly more likely to succeed, and the overall pattern of accuracy across declining SP exhibited logarithmic decay. Application: These findings suggest a "prevalence paradox" within human-machine teams. As automation reduces attack SP, the human operator becomes increasingly likely to fail in detecting and reporting attacks that remain. In the cyber realm, the potential to artificially inflict this state on adversaries, hacking the human operator rather than algorithmic defense, is considered. Specific and general information security design countermeasures are offered.

The laudable effort by Strayer and his colleagues to derive a systematic method to assess forms of cognitive distraction in the automobile is beset by the problem of nonstationary in driver response capacity. At the level of the overall goal of driving, this problem conflates actual on-road behavior; characterized by underspecified task satisficing, with our own understandable, scientifically inspired aspiration for measuring deterministic performance optimization. Measures of response conceived under this latter imperative are, at best, only shadowy reflections of the actual phenomenological experience involved in real-world vehicle control. Whether we, as a research community, can resolve this issue remains uncertain. However, we believe we can mount a positive attack on what is arguably another equally important dimension of the collision problem.

Objective The aim of this study is to describe information acquisition theory, explaining how drivers acquire and represent the information they need. Background While questions of what drivers are aware of underlie many questions in driver behavior, existing theories do not directly address how drivers in particular and observers in general acquire visual information. Understanding the mechanisms of information acquisition is necessary to build predictive models of drivers' representation of the world and can be applied beyond driving to a wide variety of visual tasks. Method We describe our theory of information acquisition, looking to questions in driver behavior and results from vision science research that speak to its constituent elements. We focus on the intersection of peripheral vision, visual attention, and eye movement planning and identify how an understanding of these visual mechanisms and processes in the context of information acquisition can inform more complete models of driver knowledge and state. Results We set forth our theory of information acquisition, describing the gap in understanding that it fills and how existing questions in this space can be better understood using it. Conclusion Information acquisition theory provides a new and powerful way to study, model, and predict what drivers know about the world, reflecting our current understanding of visual mechanisms and enabling new theories, models, and applications. Application Using information acquisition theory to understand how drivers acquire, lose, and update their representation of the environment will aid development of driver assistance systems, semiautonomous vehicles, and road safety overall.

Objective: We assess the driving distraction potential of texting with Google Glass (Glass), a mobile wearable platform capable of receiving and sending short-message-service and other messaging formats. Background: A known roadway danger, texting while driving has been targeted by legislation and widely banned. Supporters of Glass claim the head-mounted wearable computer is designed to deliver information without concurrent distraction. Existing literature supports the supposition that design decisions incorporated in Glass might facilitate messaging for drivers. Method: We asked drivers in a simulator to drive and use either Glass or a smartphone-based messaging interface, then interrupted them with an emergency brake event. Both the response event and subsequent recovery were analyzed. Results: Glass-delivered messages served to moderate but did not eliminate distracting cognitive demands. A potential passive cost to drivers merely wearing Glass was also observed. Messaging using either device impaired driving as compared to driving without multitasking. Conclusion: Glass in not a panacea as some supporters claim, but it does point the way to design interventions that effect reduced load in multitasking. Application: Discussions of these identified benefits are framed within the potential of new in-vehicle systems that bring both novel forms of distraction and tools for mitigation into the driver's seat.

Objective: We examine how transitions in task demand are manifested in mental workload and performance in a dual-task setting. Background: Hysteresis has been defined as the ongoing influence of demand levels prior to a demand transition. Authors of previous studies predominantly examined hysteretic effects in terms of performance. However, little is known about the temporal development of hysteresis in mental workload. Method: A simulated driving task was combined with an auditory memory task. Participants were instructed to prioritize driving or to prioritize both tasks equally. Three experimental conditions with low, high, and low task demands were constructed by manipulating the frequency of lane changing. Multiple measures of subjective mental workload were taken during experimental conditions. Results: Contrary to our prediction, no hysteretic effects were found after the high- to low-demand transition. However, a hysteretic effect in mental workload was found within the high-demand condition, which degraded toward the end of the high condition. Priority instructions were not reflected in performance. Conclusion: Online assessment of both performance and mental workload demonstrates the transient nature of hysteretic effects. An explanation for the observed hysteretic effect in mental workload is offered in terms of effort regulation. Application: An informed arrival at the scene is important in safety operations, but peaks in mental workload should be avoided to prevent buildup of fatigue. Therefore, communication technologies should incorporate the historical profile of task demand.

Objective The objective of this meta-analysis is to explore the presently available, empirical findings on transfer of training from virtual (VR), augmented (AR), and mixed reality (MR) and determine whether such extended reality (XR)-based training is as effective as traditional training methods. Background MR, VR, and AR have already been used as training tools in a variety of domains. However, the question of whether or not these manipulations are effective for training has not been quantitatively and conclusively answered. Evidence shows that, while extended realities can often be time-saving and cost-saving training mechanisms, their efficacy as training tools has been debated. Method The current body of literature was examined and all qualifying articles pertaining to transfer of training from MR, VR, and AR were included in the meta-analysis. Effect sizes were calculated to determine the effects that XR-based factors, trainee-based factors, and task-based factors had on performance measures after XR-based training. Results Results showed that training in XR does not express a different outcome than training in a nonsimulated, control environment. It is equally effective at enhancing performance. Conclusion Across numerous studies in multiple fields, extended realities are as effective of a training mechanism as the commonly accepted methods. The value of XR then lies in providing training in circumstances, which exclude traditional methods, such as situations when danger or cost may make traditional training impossible.