Jinchao Lin

Video Game Experience and Gender as Predictors of Performance and Stress During Supervisory Control of Multiple Unmanned Aerial Vehicles

To keep pace with increasing applications of Unmanned Aerial Vehicles (UAVs), recruitment of operators will need to be expanded to include groups not traditionally engaged in UAV pilot training. The present study may inform this process as it investigated the relationship between video game experience and gender on performance of imaging and weapon release tasks in a simulated multi-UAV supervisory control station. Each of 101 participants completed a 60 minute experimental trial. Workload and Level of Automation (LOA) were manipulated. Video gaming expertise correlated with performance on a demanding surveillance task component. Video gamers also placed more trust in the automation in demanding conditions and exhibited higher subjective task engagement and lower distress and worry. Results may encourage recruitment of UAV operators from nontraditional populations. Gamers may have a particular aptitude, and with gaming experience controlled, women show no disadvantage relative to men.

Workload Is Multidimensional, Not Unitary: What Now?

It is commonly assumed that workload is a unitary construct, but recent data suggest that there are multiple subjective and objective facets of workload that are only weakly intercorrelated. This article reviews the implications of treating workload as multivariate. Examples from several simulated task environments show that high subjective workload is compatible with a variety of patterns of multivariate psychophysiological response. Better understanding of the cognitive neuroscience of the different components of workload, including stress components, is required. At a practical level, neither subjective workload measures nor single physiological responses are adequate for evaluating task demands, building predictive models of human performance, and driving augmented cognition applications. Multivariate algorithms that accommodate the variability of cognitive and affective responses to demanding tasks are needed.

Considerations in Physiological Metric Selection for Online Detection of Operator State: A Case Study

The development of closed-loop systems is fraught with many challenges. One of the many important decisions to be made in this development is the selection of suitable metrics to detect operator state. Successful metrics can inform adaptations in an interfaces design, features, or task elements allocated to automated systems. This paper will discuss various challenges and considerations involved in the selection of metrics for detecting fatigue in operators of unmanned aerial vehicles UAVs. Using Eggemeier and colleagues guidelines for workload metric selection as a basis, we review several criteria for metric selection and how they are applied to selection of metrics designed to assess operator fatigue in an applied closed-loop system.

Automation Reliability and Other Contextual Factors in Multi-UAV Operator Selection

Multi-unmanned air vehicle (UAV) operation requires a unique set of skills and high demand for new operators requires selection from populations without previous flight training. To support developing criteria for multi-UAV operator selection, the present study investigated the role of multiple individual difference factors in performance under different multi-UAV specific contexts. Specifically, we compared performance under fatigue using a high- and low-reliability automated aid. Accuracy on surveillance tasks, as well as reliance on automation were assessed. Video gaming expertise was associated with reduced stress and less reliance with a low-reliability automated aid. Distress was the most robust predictor of performance accuracy, but high distress was harmful only when reliability was low. Personality correlates of performance varied with both automation reliability and gender. Our findings suggest that multi-UAV operator selection should take into account the reliability of the automated systems.

Resilient autonomous systems: Challenges and solutions

Advances in the technology of autonomous systems calls for an examination of the factors that confer resilience on the human-machine system. We identify challenges for teaming between human operators and autonomous systems associated with cognitive demands, trust and operator self-regulation. Solutions to these challenges partly require designing systems for effective signaling of capabilities and “intent” to the human operator. They also require selection and training of operators to team with systems that may simulate intelligent, social behaviors, as well as diagnostic monitoring of operator neurocognitive status. Implementing such solutions supports resilience at a systems level, so that machine and human can compensate for each others limitations in challenging circumstances.

Emotional intelligence and giftedness.

http://dx.doi.org/10.1037/0000038-038 APA Handbook of Giftedness and Talent, S. I. Pfeiffer (Editor-in-Chief) Copyright

The Relevance of Theory to Human-Robot Teaming Research and Development

In many disciplines and fields, theories help organize the body of knowledge in the field and provide direction for research. In turn, research findings contribute to theory building. The field of human-robot teaming (HRT) is a relatively new one, spanning only over the last two decades. Much of the research in this field has been driven by expediency rather than by theory, and relatively little effort has been invested in using HRT research to advance theory. As the field of HRT continues to expand rapidly, we find it increasingly necessary to relate theories to the research so that one can inform the other. As an initial effort, the current work will discuss and evaluate two broad research areas in human-robot teaming, and identify theories relevant to each area. The areas are (i) human-robot interfaces, and (ii) specific factors that enable teaming. In identifying the relevant theories for each area, we will describe how the theories were used and if findings supported the theories.

The Impact of Automation Reliability and Operator Fatigue on Performance and Reliance

Reliability of automation is known to influence operator reliance on automation. What is less understood is how the influence of reliability and the effects of operator fatigue might interact. The present study investigated the impact of automation reliability on accuracy and reliance and how this impact changes with level of fatigue during simulated multiple unmanned aerial vehicle (UAV) operation. Participants (N = 131) completed a two-hour simulated multi-UAV mission assisted by an automated decision making aid of either high or low reliability. A decrease in subjective task engagement and performance over time marked the induction of passive fatigue by the mission. Participants were more trusting in the high reliability condition than in the low reliability condition. Finally, reliance decreased with time at any reliability, but a significant interaction between reliability and time on task indicated that the decrease was of smaller magnitude when the automation was reliable.

Trust in Autonomous Systems for Threat Analysis: A Simulation Methodology.

Human operators will increasingly team with autonomous systems in military and security settings, for example, evaluation and analysis of threats. Determining whether humans are threatening is a particular challenge to which future autonomous systems may contribute. Optimal trust calibration is critical for mission success, but most trust research has addressed conventional automated systems of limited intelligence. This article identifies multiple factors that may influence trust in autonomous systems. Trust may be undermined by various sources of demand and uncertainty. These include the cognitive demands resulting from the complexity and unpredictability of the system, “social” demands resulting from the system’s capacity to function as a team-member, and self-regulative demands associated with perceived threats to personal competence. It is proposed that existing gaps in trust research may be addressed using simulation methodologies. A simulated environment developed by the research team is described. It represents a “town-clearing” task in which the human operator teams with a robot that can be equipped with various sensors, and software for intelligent analysis of sensor data. The functionality of the simulator is illustrated, together with future research directions.

Assessing Operator Psychological States and Performance in UAS Operations

Assessment for understanding, predicting, and improving human performance and system design is a key for human-computer interaction (HCI) research. Assessments can be behavioral, physiological, performance-based, and phenomenological. Assessments are important in a variety of domains, including unmanned vehicle operations, human-robot teaming, nuclear power plant operations, etc. This paper will discuss assessment approaches in the domain of unmanned aerial systems (UAS) operations to identify and quantify explanatory constructs, such as psychological states, workload, and performance. It will also discuss implications for evaluating improvements in human performance in UAS operations. Specifically, this paper will examine metrics that can be utilized to gauge the impact of demand factors on workload, task performance, operator dependence on automation, and stress response.