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.

Objective: The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background: There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method: Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results: Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion: Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application: Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

Automated vehicles (AVs) already navigate US highways and those of many other nations around the world. Current questions about AVs do not now revolve around whether such technologies should or should not be implemented; they are already with us. Rather, such questions are more and more focused on how such technologies will impact evolving transportation systems, our social world, and the individuals who live within it and whether such systems ought to be fully automated or remain under some form of direct human control. More importantly, how will mobility itself change as these independent operational vehicles first share and then dominate our roadways? How will the public be kept apprised of their evolving capacities, and what will be the impact of science and the communication of scientific advances across the varying forms of social media on these developments? We look here to address these issues and to provide some suggestions for the problems that are currently emerging.