The prevalence of autonomous technology is advancing at a rapid rate and is becoming more sophisticated. As this technology becomes more advanced, humans and autonomy may work together as teammates in various settings. A crucial component of teaming is trust, but to date, researchers are limited in assessing trust calibration dynamically in human-autonomy teams. Traditional methods of measuring trust (e.g., Likert scale questionnaires) capture trust after the fact or at a specific time. However, trust fluctuates, and determining what causes this might give machine designers insight into how machines can be improved upon so that operator’s trust towards the machines is more properly calibrated. This thesis aimed to assess the validity of an interaction-based metric of trust: anticipatory pushing of information. Anticipatory pushing of information refers to teammate A anticipating the needs of teammate B and pushing that information to teammate B. It was hypothesized there would be a positive relationship between the frequency of anticipatory pushing and self-reported trust scores. To test this hypothesis, text chat data and self-reported trust scores were analyzed in a previously conducted study in two different sessions (routine and degraded). Findings indicate that the anticipatory pushing of information and the self-reported trust scores between the human-human pairs in the degraded sessions were higher than the routine sessions. In degraded sessions, the anticipatory pushing of information between the human-human pairs was associated with human-human trust.

The present study sought to understand traumatic brain injuries (TBI) impact on executive function (EF) in terms of anticipation amongst individuals with a background in soccer; along with other contributing factors of EF curtailments that inhibit athletes. Within this study 57 participants, with a background in soccer (high school, collegiate, and semi-professional), completed five EF tasks: working memory, cognitive flexibility, attentional control, and anticipation; pattern detection and athletic cues (temporal occlusion). The results of this study concluded that when TBI history, gender, and soccer athletic level are factors, athletes with a soccer level of collegiate and semi-professional had decrements related to pattern detection anticipation; meaning athletes at higher levels had lower average scores on the Brixton Spatial Anticipation Test (BSAT). Additionally, female athletes showed more anticipation decrements related to athletic cues, especially those that are reliant on the initiation of judgment. Overall undiagnosed TBIs and limited understanding on how to approach rehabilitation to mitigate EF decrements, continue to impede individual autonomy amongst athletes. Keywords: Traumatic brain injury, executive function, anticipation, soccer, temporal occlusion, Brixton Spatial Anticipation Test (BSAT), collegiate, semi-professional, pattern detection, rehabilitation

In human-autonomy teams (HATs), the human needs to interact with one or more autonomous agents, and this new type of interaction is different than the existing human-to-human interaction. Next Generation Combat Vehicles (NGCVs), which are envisioned for the U.S. military are associated with the concept of HAT. As NGCVs are in the early stage of development, it is necessary to develop different training methods and measures for team effectiveness. The way team members communicate and task complexity are factors affecting team efficiency. This study analyzes the impact of two interaction strategies and task complexity on team situation awareness among 22 different teams. Teams were randomly assigned different interaction conditions and went through two missions to finish their assigned tasks. Results indicate that the team with the procedural interaction strategy had better team situation awareness according to the Coordinated Awareness of the Situation by Teams (CAST) scores on the artillery calls. However, the difference between the strategies was not found on CAST scores of perturbations, map accuracy, or Situation Awareness Global Assessment Technique (SAGAT) scores. Additionally, the impact of task complexity on the team situation awareness was not found. Implications and suggestions for future work are discussed.

Nuclear Power Plants (NPP) have complex and dynamic work environments. Nuclear safety and organizational management rely largely on human performance and teamwork. Multi-disciplinary teams work interdependently to complete cognitively demanding tasks such as outage control. The outage control period has the highest risk of core damage and radiation exposure. Thus, team coordination and communication are critically important during this period. The purpose of this thesis is to review and synthesize teamwork studies in NPPs, outage management studies, official Licensee Event Reports (LER), and Inspection Reports (IRs) to characterize team brittleness in NPP systems. Focusing on team brittleness can provide critical insights about how to increase NPP robustness and to create a resilient NPP system. For this reason, more than 900 official LERs and IRs reports were analyzed to understand human and team errors in the United States (US) nuclear power plants. The findings were evaluated by subject matter experts to create a better understanding of team cognition in US nuclear power plants. The results of analysis indicated that human errors could be caused by individual human errors, team errors, procedural errors, design errors, or organizational errors. In addition to these, some of the findings showed that number of reactors, operation year and operation mode could affect the number of reported incidents.

As urban populations increase, so does the demand for innovative transportation solutions which reduce traffic congestion, reduce pollution, and reduce inequalities by providing mobility for all kinds of people. One emerging solution is self-driving vehicles, which have been coined as a safer driving method by reducing fatalities due to driving accidents. While completely automated vehicles are still in the testing and development phase, the United Nations predict their full debut by 2030 [1]. While many resources are focusing their time on creating the technology to execute decisions such as the controls, communications, and sensing, engineers often leave ethics as an afterthought. The truth is autonomous vehicles are imperfect systems that will still experience possible crash scenarios even if all systems are working perfectly. Because of this, ethical machine learning must be considered and implemented to avoid an ethical catastrophe which could delay or completely halt future autonomous vehicle development. This paper presents an experiment for determining a more complete view of human morality and how this translates into ideal driving behaviors.
This paper analyzes responses to deviated Trolley Problem scenarios [5] in a simulated driving environment and still images from MIT’s moral machine website [8] to better understand how humans respond to various crashes. Also included is participants driving habits and personal values, however the bulk of that analysis is not included here. The results of the simulation prove that for the most part in driving scenarios, people would rather sacrifice themselves over people outside of the vehicle. The moral machine scenarios prove that self-sacrifice changes as the trend to harm one’s own vehicle was not so strong when passengers were introduced. Further defending this idea is the importance placed on Family Security over any other value.
Suggestions for implementing ethics into autonomous vehicle crashes stem from the results of this experiment but are dependent on more research and greater sample sizes. Once enough data is collected and analyzed, a moral baseline for human’s moral domain may be agreed upon, quantified, and turned into hard rules governing how self-driving cars should act in different scenarios. With these hard rules as boundary conditions, artificial intelligence should provide training and incremental learning for scenarios which cannot be determined by the rules. Finally, the neural networks which make decisions in artificial intelligence must move from their current “black box” state to something more traceable. This will allow researchers to understand why an autonomous vehicle made a certain decision and allow tweaks as needed.

In the next decade or so, there will be a shift in the industry of transportation across the world. Already today we have autonomous vehicles (AVs) tested in the Greater Phoenix area showing that the technology has improved to a level available to the public eye. Although this technology is not yet released commercially (for the most part), it is being used and will continue to be used to develop a safer future. With a high incidence of human error causing accidents, many expect that autonomous vehicles will be safer than human drivers. They do still require driver attention and sometimes intervention to ensure safety, but for the most part are much safer. In just the United States alone, there were 40,000 deaths due to car accidents last year [1]. If traffic fatalities were considered a disease, this would be an epidemic. The technology behind autonomous vehicles will allow for a much safer environment and increased mobility and independence for people who cannot drive and struggle with public transport. There are many opportunities for autonomous vehicles in the transportation industry. Companies can save a lot more money on shipping by cutting the costs of human drivers and trucks on the road, even allowing for simpler drop shipments should the necessary AI be developed.Research is even being done by several labs at Arizona State University. For example, Dr. Spring Berman’s Autonomous Collective Systems Lab has been collaborating with Dr. Nancy Cooke of Human Systems Engineering to develop a traffic testbed, CHARTopolis, to study the risks of driver-AV interactions and the psychological effects of AVs on human drivers on a small scale. This testbed will be used by researchers from their labs and others to develop testing on reaction, trust, and user experience with AVs in a safe environment that simulates conditions similar to those experienced by full-size AVs. Using a new type of small robot that emulates an AV, developed in Dr. Berman’s lab, participants will be able to remotely drive around a model city environment and interact with other AV-like robots using the cameras and LiDAR sensors on the remotely driven robot to guide them.
Although these commercial and research systems are still in testing, it is important to understand how AVs are being marketed to the general public and how they are perceived, so that one day they may be effectively adopted into everyday life. People do not want to see a car they do not trust on the same roads as them, so the questions are: why don’t people trust them, and how can companies and researchers improve the trustworthiness of the vehicles?

The knowledge of cognitive processes of teams and how they work as a system, has drastically broadened in recent years. However, few researchers have applied their findings to an orchestral setting. In the current study, team cognition was observed and analyzed based off an 8th grade orchestra, in addition to the middle and highest-level orchestras at a junior high and high school in the Arizona Public School system. It was found, that in the 8th grade orchestra, most communication is either given or received in the form of auditory cues both verbal and musical. Regardless of skill level, groups that have higher interactions during practices have better performances.

Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with converting an office to an open plan can foster innovation and creativity, or they can be distracting and harm the performance of employees. Through simulation, the impact of different types of office noise was studied along with other changing conditions such as number of people in the office. When productivity per person, defined in terms of mood and focus, was measured, it was found that the effect of noise was positive in some scenarios and negative in others. In simulations where employees were performing very similar tasks, noise (and its correlates, such as number of employees), was beneficial. On the other hand, when employees were engaged in a variety of different types of tasks, noise had a negative overall effect. This indicates that workplaces that group their employees by common job functions may be more productive than workplaces where the problems and products that employees are working on are varied throughout the workspace.

Behavior traits were examined in an observed experiment with the presentation of an American Pit Bull Terrier. The experiment was conducted at two locations (Wal Mart, Pet Smart) with searching for behavior traits (positive, negative) with an American Pit Bull Terrier present. In contrast to the hypothesis, there was more positive behavior traits than negative behavior traits. Together, these findings suggest that the presentation of an American Pit Bull Terrier has a more positive outlook on the breed rather than negative. Similar studies should be conducted to change the legislation in regard of "Pit Bulls" that cause discrimination against the breed.

Improvised explosive devices (IEDs) have become a major threat to military personnel in recent years. In the United States Army, Mission Payload Operators (MPOs) operate cameras from unmanned aerial vehicles (UAVs) to detect the threat of IEDs using real-time images received. Previous researchers obtained the expert knowledge of twelve MPOs at Fort Huachuca and learned that they rely on "behavioral signatures," the behavioral and environmental cues associated with IED threat rather than the IED itself (Cooke, Hosch, Banas, Hunn, Staszewski & Fensterer, 2010). To the best of our knowledge, no formal MPO training exists and all training is acquired on-the-job. The end goal is to create training systems for future MPOs using cognitive engineering based on expert skill (CEBES) that focus on detection of behavioral cues associated with IED threats. The complexity and dynamicity of cues associated with IED emplacement is to be noted, as such cues are influenced by sociocultural knowledge and often develop over significant periods of time. A dynamic full motion video simulation environment has been created, and embedded with cues elicited from expert MPOs. A three-part simulation has been created. The next step is verifying critical cues MPOs identify and focus on using eye tracking equipment.