EEG and fNIRS are associated with situation awareness (hazard) prediction during a driving task.

Situation awareness (SA) is important in many demanding tasks (e.g. driving). Assessing SA during training can indicate whether someone is ready to perform in the real world. SA is typically assessed by interrupting the task to ask questions about the situation or asking questions after task completion, assessing only momentary SA. An objective and continuous means of detecting SA is needed. We examined whether neurophysiological sensors are useful to objectively measure Level 3 SA (projection of events into the future) during a driving task. We measured SA by the speed at which participants responded to SA questions and the accuracy of responses. For EEG, beta and theta power were most sensitive to SA response time. For fNIRS, oxygenated haemoglobin (HbO) was most sensitive to accuracy. This is the first evidence to our knowledge that neurophysiological measures are useful for assessing Level 3 SA during an ecologically valid task.

We examine whether neurophysiological sensors are useful to objectively measure Level 3 situation awareness (SA) prediction during a driving task. EEG theta and beta, and fNIRS oxygenated haemoglobin were most sensitive to SA accuracy. This is evidence that neurophysiological measures can be used to assess hazard prediction (Level 3 SA).

Ironies of artificial intelligence.

Bainbridge's Ironies of Automation was a prescient description of automation related challenges for human performance that have characterised much of the 40 years since its publication. Today a new wave of automation based on artificial intelligence (AI) is being introduced across a wide variety of domains and applications. Not only are Bainbridge's original warnings still pertinent for AI, but AI's very nature and focus on cognitive tasks has introduced many new challenges for people who interact with it. Five ironies of AI are presented including difficulties with understanding AI and forming adaptations, opaqueness in AI limitations and biases that can drive human decision biases, and difficulties in understanding the AI reliability, despite the fact that AI remains insufficiently intelligent for many of its intended applications. Future directions are provided to create more human-centered AI applications that can address these challenges.

Artificial Intelligence (AI) creates many new challenges for human interaction. Five ironies of AI are discussed that limit its ultimate success, and future directions are provided to create more human-centered AI applications that can address these challenges.

The Effects of Virtual Reality, Augmented Reality, and Mixed Reality as Training Enhancement Methods: A Meta-Analysis.

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.

A Systematic Review and Meta-Analysis of Direct Objective Measures of Situation Awareness: A Comparison of SAGAT and SPAM.

OBJECTIVE: To examine evidence of sensitivity, predictiveness, and methodological concerns regarding direct, objective measures of situation awareness (SA). BACKGROUND: The ability to objectively measure SA is important to the evaluation of user interfaces and displays, training programs, and automation initiatives, as well as for studies that seek to better understand SA in both individuals and teams. A number of methodological criticisms have been raised creating significant confusion in the research field. METHOD: A meta-analysis of 243 studies was conducted to examine evidence of sensitivity and predictiveness, and to address methodological questions regarding Situation Awareness Global Assessment Technique (SAGAT), Situation Present Assessment Technique (SPAM), and their variants. RESULTS: SAGAT and SPAM were found to be equally predictive of performance. SPAM (64%) and real-time probes (73%) were found to have significantly lower sensitivity in comparison to SAGAT (94%). While SAGAT was found not to be overly memory reliant nor intrusive into operator performance, SPAM resulted in problems with intrusiveness in 40% of the studies examined, as well as problems with speed-accuracy tradeoffs, sampling bias, and confounds with workload. Concerns about memory reliance, the utility of these measures for assessing Team SA, and other issues are also addressed. CONCLUSION: SAGAT was found to be a highly sensitive, reliable, and predictive measure of SA that is useful across a wide variety of domains and experimental settings. APPLICATION: Direct, objective SA measurement provides useful and diagnostic insights for research and design in a wide variety of domains and study objectives.

Combating Information Attacks in the Age of the Internet: New Challenges for Cognitive Engineering.

OBJECTIVE: This article provides an overview of the characteristics of misinformation and information attack and their effects on the perceptions of the public, with the objective of outlining potential solutions and needed research for countering this growing problem. BACKGROUND: Society is facing a significant challenge from the spread of misinformation through websites and social media that has driven a divergence in people's perceptions and understanding of basic facts associated with many issues relevant to public policy decisions, including the economy, taxation, and the deficit; climate change and the environment; and vaccinations and public health and safety. A number of factors are driving this fracture, including information presentation challenges that lead to poor information understanding, deliberate information attacks, social network propagation, poor assessments of information reliability, and cognitive biases that lead to a rejection of information that conflicts with preexisting beliefs. RESULTS: A framework for understanding information attack is provided, including common sources, features, avenues, cognitive mechanisms, and major challenges in overcoming information attacks. CONCLUSION AND APPLICATION: Potential solutions and research needs are presented for improving people's understanding of online information associated with a wide range of issues affecting public policy.

From Here to Autonomy.

As autonomous and semiautonomous systems are developed for automotive, aviation, cyber, robotics and other applications, the ability of human operators to effectively oversee and interact with them when needed poses a significant challenge. An automation conundrum exists in which as more autonomy is added to a system, and its reliability and robustness increase, the lower the situation awareness of human operators and the less likely that they will be able to take over manual control when needed. The human-autonomy systems oversight model integrates several decades of relevant autonomy research on operator situation awareness, out-of-the-loop performance problems, monitoring, and trust, which are all major challenges underlying the automation conundrum. Key design interventions for improving human performance in interacting with autonomous systems are integrated in the model, including human-automation interface features and central automation interaction paradigms comprising levels of automation, adaptive automation, and granularity of control approaches. Recommendations for the design of human-autonomy interfaces are presented and directions for future research discussed.

Situation awareness in anesthesia: concept and research.

Accurate situation awareness (SA) of medical staff is integral for providing optimal performance during the treatment of patients. An understanding of SA and how it affects treatment of patients is therefore crucial for patient safety and an essential element for research on human factors in anesthesia. This review describes the concept of SA in the anesthesia environment, including the interaction with associated medical teams. Different approaches for its assessment in the work environment of anesthesia are provided. Factors contributing to expertise in SA are described and approaches for the training of SA in anesthesia are discussed, as are types of errors that occur during the development of SA. Finally, the authors briefly present strategies to improve SA during daily anesthesia practice through altered designs of monitor displays.

Collaborative planning and situation awareness in Army command and control.

We conducted a theoretical investigation of a complex command and control (C2) operation--the manoeuvres planning processes in Army land-battle situations, to improve understanding of how technology can best be designed to support planning and course of action development. We drew upon results from cognitive task analyses and interviews with subject matter experts and insights gleaned from observations of Army training exercises and experiments to make inferences on the C2 activities carried out in preparation for tactical manoeuvres. In this paper, we summarize several critical human factors issues associated with planning in a rapidly evolving environment, as identified in our investigation, and describe system design concepts aimed at addressing these challenges to distributed collaborative planning of C2 activities. We conclude with implications for the application of these findings to other C2 domains.