Ryan Wohleber

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.

Intelligent Agent Transparency The Design and Evaluation of an Interface to Facilitate Human and Intelligent Agent Collaboration

We evaluated the usability and utility of an unmanned vehicle management interface that was developed based on the Situation awareness–based Agent Transparency model. We sought to examine the effect of increasing levels of agent transparency on operator task performance and perceived usability of the agent. Usability and utility were assessed through flash testing, a focus group, and experimental testing. While usability appeared to decrease with the portrayal of uncertainty, operator performance and reliance on key parts of the interface increased. Implications and next steps are discussed.

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.

Individual Differences in Resilience and Affective Response during Simulated UAV Operations

This study investigated the consistency of stress responses to different sources of stress during simulated multiple unmanned aerial vehicle (UAV) operation. Of particular interest is whether or not resilience is a general factor that predicts stress response across different stressors. Participants completed two 10-minute test conditions designed to induce stress − one through high workload and another through negative evaluation. DSSQ factors gauged subjective stress response and physiological measures gauged objective stress response. Three trait resilience measures were predictors of these stress responses. Results revealed that different resilience scales and subscales predicted different response components. These findings suggest that individuals vary in their vulnerability to specific stressors. Training and personnel selection could thus benefit from a comprehensive, multifaceted assessment of resilience.

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 Intellegence and Decision Making Under Stress

This study investigated the protective effects of emotional intelligence (EI) during decision-making under stress. The researchers assigned 172 participants to either a negative or neutral feedback group and assessed EI, distress, information search prior to choice, and decision-making performance. We predicted EI would be associated with superior decision-making following negative performance feedback. Statistical analysis revealed negative feedback significantly increased distress and was associated with poorer decision-making for easy decisions. Also, EI was indirectly associated with decision-making performance. Participants high in EI accessed decision relevant information more frequently prior to decision-making and greater search frequency predicted superior decision making.

Individual differences in driver over-confidence: implications for stress, error and managing impairments

This study aimed to extend understanding of individual differences in over-confidence in driver safety. First, we discriminated general driving confidence from confidence in coping with impairments such as fatigue and distraction. Second, we discriminated three aspects of overconfidence specified in terms of a recent Bayesian belief updating model of over-confidence (Moore and Healy, 2008): overestimation, overplacement and overprecision. Calibration tasks were used for this purpose. Results showed that the magnitude of overconfidence differed across the various metrics, and different metrics were only modestly intercorrelated. Confidence in handling impairment appears to be distinct from general confidence. Both forms of confidence were negatively related to dislike of driving but related differently to other aspects of driver behavior and stress. Risk factors related to self-estimation (violations, thrill-seeking) may be distinct from factors related to overplacement (aggression). Discrimination of multiple metrics for driving overconfidence may support better matching of safety interventions to the individual driver.

Eye Tracking Metrics for Insider Threat Detection in a Simulated Work Environment

Insider Threats (ITs) are hard to identify because of their knowledge of the organization and motivation to avoid detection. One approach to detecting ITs utilizes Active Indicators (AI), stimuli that elicit a characteristic response from the insider. The present research implemented this approach within a simulation of financial investigative work. A sequence of AIs associated with accessing a locked file was introduced into an ongoing workflow. Participants allocated to an insider role accessed the file illicitly. Eye tracking metrics were used to differentiate insiders and control participants performing legitimate role. Data suggested that ITs may show responses suggestive of strategic concealment of interest and emotional stress. Such findings may provide the basis for a cognitive engineering approach to IT detection.