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.

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.

Cyber security operators in the military and civilian sector face a lengthy repetitive work assignment with few critical signal occurrences under conditions in which they have little control over what transpires. In this sense, their task is similar to vigilance tasks that have received considerable attention from human factors specialists in regard to other operational assignments such as air traffic control, industrial process control, and medical monitoring. Accordingly, this study was designed to determine if cyber security tasks can be linked to more traditional vigilance tasks in regard to several factors known to influence vigilance performance and perceived mental workload including time on task, the probability of critical signal occurrence, and event rate (the number of stimulus events that must be monitored in order to detect critical signals). Consistent with the results obtained in traditional vigilance experiments, signal detection on a 40-minute simulated cyber security task declined significantly over time, was directly related to signal probability, and inversely related to event rate. In addition, as in traditional vigilance tasks, perceived mental workload in the cyber task, as reflected by the NASA Task Load Index, was high. The results of this study have potential meaning for designers of cyber security systems in regard to psychophysical factors that might influence task performance and the need to keep the workload of such systems from exceeding the information processing bounds of security operators.

In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading , we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding, which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in words-per-minute (WPM)) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this article.

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.