Automation is becoming more autonomous, and the application of automation as a collaborator continues to be explored. A major restriction to automation’s application as a collaborator is that people often hold inaccurate expectations of their automated collaborator. Goal alignment has been shown to be beneficial in collaborations and delegation decisions among human-human and human-automation collaborations. Few studies have investigated the difference that goal alignment has on human collaborators compared to automated collaborators. In this 2 (goal aligned or misaligned) x 2 (human or automated) between-subjects study, participants complete a simplified triage patient task and then are given the opportunity to stay with their manual task solution or to delegate their decision and go with their collaborator’s recommendation. Participants never delegated to collaborators with goals that were not aligned to theirs. Participants working with human collaborators that have similar goals to them were more often delegated to and more often associated with a better triage performance. These results can inform the design of similar systems that foster collaboration and achieve better team performance. Although goal alignment was crucial for delegation decisions, it was difficult to achieve complete agreement of goals. Future research should investigate effective methods to better communicate goals among collaborators.

A key contribution of human factors engineering is the concept of workload: a construct that represents the relationship between an operator’s cognitive resources, the demands of their task, and performance. Understanding workload can lead to improvements in safety and performance for people working in critical environments, particularly within action teams. Recently, there has been interest in considering how the workload of a team as a whole may differ from that of an individual, prompting investigation into team workload as a distinct team-level construct. In empirical research, team-level workload is often considered as the sum or average of individual team members' workloads. However, the intrinsic characteristics of action teams—such as interdependence and heterogeneity—challenge this assumption, and traditional methods of measuring team workload might be unsuitable. This dissertation delves into this issue with a review of empirical work in action teams, pinpointing several gaps. Next, the development of a testbed is described and used to address two pressing gaps regarding the impact of interdependence and how team communications relate to team workload states and performance. An experiment was conducted with forty 3-person teams collaborating in an action team task. Results of this experiment suggest that the traditional way of measuring workload in action teams via subjective questionnaires averaged at the team level has some major shortcomings, particularly when demands are elevated, and action teams are highly interdependent. The results also suggested that several communication measures are associated with increases in demands, laying the groundwork for team-level communication-based measures of team workload. The results are synthesized with findings from the literature to provide a way forward for conceptualizing and measuring team workload in action teams.

Urban search and rescue (USAR) teams may use Artificial Social Intelligence (ASI) agents to aid teams in adapting to dynamic environments, minimize risk, and increase mission assurance and task performance. This thesis underlines the relationship between vocal pitch, stress, and team performance from a recent experiment conducted in a simulated USAR synthetic task environment (STE). The simulated USAR-STE is a platform to use ASI as an advisor to intervene in the human team members’ cognitive processes, which aims to reduce risk to task execution and to maintain team performance. Three heterogeneous and interdependent roles interact via voice communication to search and rescue the victims: (1) medic -rescues victims and identifies the severity of injuries; (2) transporter -moves victims to their designated zone based on injury severity; (3) engineer -removes hazardous material such as rubble from a room or hallway that is blocking passage. Different speeds are associated with each role, such as medic, transporter, and engineer. Medic has a default speed; the transporter has times two over the default speed; the engineer has the slowest speed. In a total of 45 teams, three ASI conditions, manipulated based on ASI intervention communication length and frequency, were analyzed. Each team participated in two 15-min missions. The results indicate a U-shaped relationship between the transporter’s pitch and a change in team performance. A possible explanation for this significance is the task and role design. The transporter may have the most central role in voice communication because when the transporter is under varying levels of workload and stress, and thus voice pitch has a complex relationship with performance for that role.

The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent can have an impact on learning outcomes and perceptions of the instructor. However, these outcomes and perceptions have yet to be fully understood in the context of a virtual human instructor. Outcome measures collected included: knowledge retention, knowledge transfer, and cognitive load. Perception measures were collected using the Agent Persona Instrument-Revised, API-R, and a speaker-rating survey. Overall, there were no significant differences between the accented conditions. However, the synthetic condition had significantly lower knowledge retention, knowledge transfer, and mental effort efficiency than the professional voices in the human condition. Participants rated the human recordings higher on speaker-rating and API-R measures. These findings demonstrate the importance of considering the quality of the voice when designing multimedia learning environments.

Human-robot teams (HRTs) have seen more frequent use over the past few years,specifically, in the context of Search and Rescue (SAR) environments. Trust is an important factor in the success of HRTs. Both trust and reliance must be appropriately calibrated for the human operator to work faultlessly with a robot teammate. In highly complex and time restrictive environments, such as a search and rescue mission following a disaster, uncertainty information may be given by the robot in the form of confidence to help properly calibrate trust and reliance. This study seeks to examine the impact that confidence information may have on trust and how it may help calibrate reliance in complex HRTs. Trust and reliance data were gathered using a simulated SAR task environment for participants who then received confidence information from the robot for one of two missions. Results from this study indicated that trust was higher when participants received confidence information from the robot, however, no clear relationship between confidence and reliance were found. The findings from this study can be used to further improve human-robot teaming in search and rescue tasks.

ABSTRACT Billions of dollars are spent annually on urine specimen collection and analysis as they are critical clinical components vital to human health. The mid-stream clean catch (MSCC) process is the gold standard of ambulatory urine specimen collection for clinical diagnosis of urinary tract infections (UTI). The MSCC process is over 60 years old and is plagued by ridiculously high specimen contamination rates. The MSCC has resisted numerous attempts aimed at improving it. The purpose of this study was to determine if utilizing the concepts of Human Systems Engineering (HSE) could improve the urine specimen collection process. HSE concepts were not only targeted toward the problems, they were also used in the quest to develop effective solutions. Results obtained demonstrate that HSE concepts, when applied to urine specimen collection, can and do make a difference in terms of specimen quality and patient satisfaction. One low cost easily implemented targeted HSE-informed intervention effort resulted in a specimen contamination rate reduction of 16.6%. A second targeted HSE-informed intervention involving the redesign of the specimen cup, its instruction set, and additional sign placement made it three times less likely for participants to provide a contaminated MSCC sample. The redesigned specimen cup automatically captures and isolates an initial void sample from an MSCC sample, both derived from one continuously provided patient specimen. Clinical utility comes in the form of improved MSCC specimen quality and a separated initial void available for analysis using Nucleic Acid Amplification Testing (NAAT) or other test protocols. Capturing and isolating both an initial void and an MSCC at the same time allows for a more complete diagnostic workup utilizing a higher quality MSCC without requiring the patient to follow two different protocols to urinate into two different specimen cups. The redesigned specimen cup also provides for automatic overflow prevention, incorporates a new ergonomic grip, and a saddle adapter that provides affordances for both women and men in terms of urine capture and the reduced likelihood of urinating on one’s self.