With the rise of the Fourth Industrial Revolution, technology has and will exponentially transform in a way that will change how humans live, function, and work. This notion is profoundly noticeable in the realm of military human-agent teaming (HAT), where intelligent systems are used in critical missions to collaborate with Soldiers on a mission. With any new phenomenon in the natural world, a theoretical model is required to understand and predict behavior. In this paper, we present a literature review to explore a proposed model of simulated military human-agent teaming that centers around task performance.

Cyber challenges faced by organizations today involve malicious inside actors, often labeled insider threats (ITs). These present a difficult challenge in that the most well-designed cybersecurity apparatus is vulnerable to those inside the organization who have privileged access to information systems. Innovative methods must be developed to help security analysts narrow the large pool of potential ITs in large organizations to a more manageable number. The purpose of this article is to develop and validate eye-tracking metrics that are diagnostic of IT behavior. Key stimuli, or called active indicator probes, were embedded into a simulated workflow to elicit diagnostic eye-tracking responses. Two environments were simulated: financial and intelligence analysis. We evaluated participants performing as regular workers relative to ITs to identify metrics that distinguished between the two groups. Detection of illicit eye gaze behavior while using chat programs was possible when conversations with accomplices occurred in a separate chat window from normal permissible chat conversations. Validation of results in real work environments is necessary for practical application. However, if the approach proves to translate successfully, automated monitoring of eye-tracking responses may augment existing insider detection methods, within frameworks for best practices in organizational security and cyber defense.

In the past few years, the ethics and transparency of AI and other digital systems have received much attention. There is a vivid discussion on explainable AI, both among practitioners and in academia, with contributions from diverse fields such as computer science, humancomputer interaction, law, and philosophy. Using the Value Sensitive Design (VSD) method as a point of departure, this paper explores how VSD can be used in the context of transparency. More precisely, it is investigated (i) if the VSD Envisioning Cards facilitate transparency as a pro-ethical condition, (ii) if they can be improved to realize ethical principles through transparency, and (iii) if they can be adapted to facilitate reflection on ethical principles in large groups. The research questions are addressed through a two-fold case study, combining one case where a larger audience participated in a reduced version of VSD with another case where a smaller audience participated in a more traditional VSD workshop. It is concluded that while the Envisioning Cards are effective in promoting ethical reflection in general, the realization of ethical values through transparency is not always similarly promoted. Therefore, it is proposed that a transparency card be added to the Envisioning Card deck. It is also concluded that a lightweight version of VSD seems useful in engaging larger audiences. The paper is concluded with some suggestions for future work.

The basic building block of any eye tracking research is the eye fixations. These eye fixations depend on more fine data gathered by the eye tracker device, the raw gaze data. There are many algorithms that can be used to transform the raw gaze data into eye fixation. However, these algorithms require one or more thresholds to be set. A knowledge of the most appropriate values for these thresholds is necessary in order for these algorithms to generate the desired output. This paper examines the effect of a set of different settings of the two thresholds required for the identification-dispersion threshold type of algorithms: the dispersion and duration thresholds on the generated eye fixations. Since this work is at its infancy, the goal of this paper is to generate and visualize the result of each setting and leave the choice for the readers to decide on which setting fits their future eye tracking research.

Head mounted displays (HMD) are becoming ubiquitous. Simulator sickness has been an issue since the first simulators and HMDs were created. As computational power and display capabilities increase, so does their utilization in technologies such as HMDs. However, this does not mean that the issues that once plagued these systems are now obsolete. In fact, evidence suggests that these issues have become more prevalent. Whether the system is Augmented Reality (AR), Virtual Reality (VR), or Mixed Reality (MR) the issues associated with simulator sickness or cybersickness have become more widespread. The reasons are uncertain, but probably multiple. One possible reason is the concept of vection, which is the illusion of movement to the participant where there is none physically. Vection plays a vital role in immersion and presence, however; it is also integral in simulator sickness. Another potential reason is the availability of HMDs. Traditionally a tool used in military training or laboratory settings, HMDs have now become a consumer item. This work reviews the current state of HMD issues such as simulator sickness or cybersickness. It reviews the similarities and differences of the sickness states that are commonly found with HMDs. Also, terms such as presence and immersion are delineated so they are used appropriately. The current theories on simulator sickness and cybersickness are reviewed. Further, the measurement and mitigation strategies currently being employed to reduce sickness are reviewed. Lastly, suggestions for more accurate measurement are recommended.

In forty years, human existence will be radically transformed by advances in information technology, including Artificial Intelligence, robots capable of social agency, and other autonomous physical and virtual systems. Future personality research must assess, understand, and apply individual differences in adaptation to these novel challenges. This review article discusses directions for future personality research. Cross-cultural research provides a model, in that both universal traits and those specific to future society are needed. Evolution of major "etic" trait models of today will maintain their relevance. There is also scope for defining a range of new "emic" dimensions for constructs such as trust in autonomy, mental models for robots, anthropomorphism of technology, and preferences for communication with machines. A more revolutionary perspective is that availability of big data on the individual will revive idiographic perspectives. Both nomothetic and idiographic accounts of personality may support applications such as design of intelligent systems and products that adapt to the individual.

Potential benefits of technology such as automation are oftentimes negated by improper use and application. Adaptive systems provide a means to calibrate the use of technological aids to the operator’s state, such as workload state, which can change throughout the course of a task. Such systems require a workload model which detects workload and specifies the level at which aid should be rendered. Workload models that use psychophysiological measures have the advantage of detecting workload continuously and relatively unobtrusively, although the inter-individual variability in psychophysiological responses to workload is a major challenge for many models. This study describes an approach to workload modeling with multiple psychophysiological measures that was generalizable across individuals, and yet accommodated inter-individual variability. Under this approach, several novel algorithms were formulated. Each of these underwent a process of evaluation which included comparisons of the algorithm’s performance to an at-chance level, and assessment of algorithm robustness. Further evaluations involved the sensitivity of the shortlisted algorithms at various threshold values for triggering an adaptive aid.

In the dismounted military field, robots and unmanned vehicles are increasingly used as force multipliers and teammates. As such, a fluent human-robot interaction (HRI) becomes vital and is stimulated by fitting the robot and its interface to the human teammate’s capabilities. This is where individual differences of the human needs to be considered, such as those found in spatial ability. In HRI, information presented to the human teammate requires mental manipulation and interpretation to inform subsequent human actions, which relies on spatial ability. In order to generalize findings to the armed forces and to inform future design requirements, factors pertaining to construct operationalization, measurement, and task type need to be examined. The aim of the present literature review is to investigate spatial ability findings in military HRI. In this review, metadata over the past decade are synthesized in light of a formal factor analysis of spatial ability [8]. The results show that the operationalizations of spatial ability are alarmingly divergent in the research field of military/UxV HRI. The relationship between spatial ability and task performance in our findings is complicated by a lack of standardized assessments of spatial ability and a small sample size, which is an indication of the current state of affairs. However, there is a conservative indication of a relationship between aspects of spatial ability and primary military reconnaissance tasks. As an effort to inform future studies, this literature review concludes with recommendations for military-affiliated research and development, to enhance the measurement, validation, and generalization of findings of an individual factor that has the potential to benefit a fluent HRI and the transition of robots from tools to teammates.

Holographic technologies allow for direct three-dimensional (3D) imaging without the need for special glasses or headwear. Holographic imaging ranges from static (i.e., unchanging) toward dynamic (i.e., changing) presentations. Since dynamic holographic products are in their developmental infancy, this study utilized static holographic images to predict future needs and preferences for dynamic holography. Using a single anatomical model, five static holograms were created for subjective evaluation from respondents. Four major research questions addressed the aim of this study, to determine the impact of color, hogel size, polygon density, and directional resolution on user preferences and perceived image quality of holograms within the medical field.

Analog, full-scope, full-scale simulators with the fidelity to simulate all of the physical and underlying thermodynamics in the real system are representative of training simulators used by current operating nuclear power plants. However, digital simulators are becoming desirable to researchers and utility companies alike due to their increased accessibility and the capability of integrating new system upgrades. The present study compared operators’ workload response in a given operating procedure using an analog, full-scope/scale simulator and a digital, part-task simulator. Subjective measures (NASA-TLX, MRQ, ISA) and physiological measures (electrocardiography) were used to profile workload response. The results suggested the feasibility of using digital simulators for research purposes with potential future implications for training.

Current cybersecurity research on insider threats has focused on finding clues to illicit behavior, or "passive indicators", in existing data resources. However, a more proactive view of detection could preemptively uncover a potential threat, mitigating organizational damage. Active Indicator Probes (AIPs) of insider threats are stimuli placed into the workflow to trigger differential psychophysiological responses. This approach requires defining a library of AIPs and identifying eye tracking metrics to detect diagnostic responses. Since studying true insider threats is unrealistic and current research on deception uses controlled environments which may not generalize to the real world, it is crucial to utilize simulated environments to develop these new countermeasures. This study utilized a financial work environment simulation, where participants became employees reconstructing incomplete account information, under two conditions: permitted and illicit cyber tasking. Using eye tracking, reactions to AIPs placed in work environment were registered to find metrics for insider threat.

The effect of task switching on performance has been examined in many different fields and contexts. Sudden changes in task load can significantly impair performance, which can have detrimental consequences in dismounted military operations. As technology is advancing, robots are sought to take on the role of a teammate to the human soldier in the field. Robot-to-human communication modality may need to switch when mayhem occurs in military missions. Modality switching has been associated with performance decrements, although these effects are largely unknown in military human-robot teaming situations. The present study examined the cost associated with switching task demand and robot-to-human communication modality type on performance in a simulated cordon-and-search mission. The results showed that switches in task load affected threat detection performance. Auditory reporting increased performance more than visual reporting in low-after-high task load epochs. Performance with auditory reports was also higher in high-after-low demand blocks than low-after-high. The effect of switching needs to be taken into account for high-stakes human-robot interactions.

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.

With technological developments in robotics and their increasing deployment, human-robot teams are set to be a mainstay in the future. To develop robots that possess teaming capabilities, such as being able to communicate implicitly, the present study implemented a closed-loop system. This system enabled the robot to provide adaptive aid without the need for explicit commands from the human teammate, through the use of multiple physiological workload measures. Such measures of workload vary in sensitivity and there is large inter-individual variability in physiological responses to imposed taskload. Workload models enacted via closed-loop system should accommodate such individual variability. The present research investigated the effects of the adaptive robot aid vs. imposed aid on performance and workload. Results showed that adaptive robot aid driven by an individualized workload model for physiological response resulted in greater improvements in performance compared to aid that was simply imposed by the system.

When technology opens up new domains or areas of research, such as human-agent teaming, new challenges in assessments emerge. Assessments may not be as systematically conducted as new measures develop, and the research may not be as firmly grounded in theory since theories in newer domains are still being formulated. As a result, research in these domains can be fragmented. To address these, an empirically-driven network approach that is complementary to the traditional theory-driven approach is proposed. The network approach seeks to discover patterns and structure in the assessment metadata (.e.g., constructs and measures) that can provide starting points and direction for future research. This paper outlines the workflow of the network approach which comprises three steps: (1) Data Preparation; (2) Data Analysis; and (3) Structure Discovery. As most of the work has been on Data Preparation, the paper will focus on the complexities and issues encountered in the first step, and include broad overviews of the subsequent steps. Anticipated use and outcomes of the network approach are also discussed.

Motivation is a key factor for learning and retention. Motivation in learning, which refers to an individual's desire to learn, is influenced by a number of factors (e.g., interest, self-regulation abilities, self-efficacy, personality) and is further complicated by an individual's sensitivity to those factors. Thus, identifying a learner's general and fine-grained motivation factors is essential to designing individualized adaptations or interventions for implementation in an Intelligent Tutoring System (ITS). The present study addressed the development and validation of the Motivational Assessment Tool to identify correlations between motivation variables and factors from education and psychology. The results indicate an overlap between the scales, which implies a higher-order dimension structure not captured by existing instruments, enabling instructional designers to use the MAT to evaluate the motivation support provided by an ITS overall and identify motivation needs for individual learners.

The global reach of the US military requires commanders to manage multinational teams effectively but cultural factors make effective decision-making challenging. This article reports a series of three studies with a total sample of 696 participants. They examined how sociocultural factors, personality traits, and decision-making competencies correlated with performance on a Situation Judgment Test (SJT) for military multinational decision -making. Predictors of SJT performance included general decision-making competencies, low nationalism, and Big Five and Dark Triad personality traits. Higher cultural intelligence (CQ) did not predict the SJT. Nationalism was associated with poorer decision-making in general, as well as traits associated with social agency. Regression analyses suggested that multiple dimensions predicted performance independently. Several factors linked to poor performance were associated with high confidence. Lack of cognitive flexibility may also contribute to impairments. Multivariate assessments of commanders may be utilized to guide training towards the individual's specific vulnerabilities.

Recent developments in the Internet of Things (IoT), social media, and the data sciences have resulted in larger volumes of data than ever before, offering more opportunity for observing and understanding behaviors. Advances in data analytic and machine learning techniques have also enabled assessments to be more multi-faceted, incorporating data from more sources. Machine learning algorithms such as Decision Trees and Random Forests, K-nearest neighbors, and Artificial Neural Networks have been used to uncover hidden patterns in data and derive predictions and recommendations from a wide range of data types and sources. However, these do not necessarily yield insights into behaviors in complex systems/domains. Methods from mathematics such as Set Theory, Graph Theory, and Network Science may be useful in shedding light on the interactions and relationships within and across domains. This paper provides a description of the applications, strengths, and limitations of some of these techniques and methods.