Raja Parasuraman

Oxytocin selectively increases perceptions of harm for victims but not the desire to punish offenders of criminal offenses

The neuropeptide oxytocin functions as a hormone and neurotransmitter and facilitates complex social cognition and approach behavior. Given that empathy is an essential ingredient for third-party decision-making in institutions of justice, we investigated whether exogenous oxytocin modulates empathy of an unaffected third-party toward offenders and victims of criminal offenses. Healthy male participants received intranasal oxytocin or placebo in a randomized, double-blind, placebo-controlled, between-subjects design. Participants were given a set of legal vignettes that described an event during which an offender engaged in criminal offenses against victims. As an unaffected third-party, participants were asked to rate those criminal offenses on the degree to which the offender deserved punishment and how much harm was inflicted on the victim. Exogenous oxytocin selectively increased third-party decision-makers perceptions of harm for victims but not the desire to punish offenders of criminal offenses. We argue that oxytocin promoted empathic concern for the victim, which in turn increased the tendency for prosocial approach behavior regarding the interpersonal relationship between an unaffected third-party and a fictional victim in the criminal scenarios. Future research should explore the context- and person-dependent nature of exogenous oxytocin in individuals with antisocial personality disorder and psychopathy, in whom deficits in empathy feature prominently.

A Comprehensive Methodology for Assessing Human-Robot Team Performance for Use in Training and Simulation:

New methodologies and quantitative measurements for evaluating human-robot team performance must be developed to achieve effective coordination between teams of humans and unmanned vehicles. The Mixed Initiative Team Performance Assessment System (MITPAS) provides such a comprehensive measurement methodology. MITPAS consists of a methodology, tools and procedures to measure the performance of mixed manned and unmanned teams in both training and real world operational environments. This paper describes MITPAS and the results of an initial experiment conducted to validate the measures and gain insight into the effect of robot competence on operator trust as well as on human-robot team performance.

Modeling Human-Automation Team Performance in Networked Systems: Individual Differences in Working Memory Count

As human-machine systems grow in size and complexity, there is a need to understand and model how human attentional limitations affect system performance, especially in large networks. As a first step, human-in-the-loop experiments can provide the requisite data. Secondly, such data can be modeled to provide insights by predicting performance with a large number of vehicles. Accordingly, we first carried out an experiment examining human-UAV system performance under low and high levels of task load. We also examined the effects of a networked environment on performance by manipulating the number and relevance of network message traffic from automated agents. Results showed that in conditions of high task load, performance degraded. Moreover, performance increased with the help of relevant messages, and decreased with irrelevant, noise messages. Furthermore, a simple correlation showed a fairly strong connection between working memory scores and our collected performance data. Using regression to model this data revealed that a simple linear equation does not provide for very accurate modeling of different aspects of decision making performance. However, inclusion of the OSPAN working memory capacity measure improves prediction capability considerably. Together, the results of this study show that human-automation team performance metrics can be modeled and used to predict performance under varying levels of traffic, probability of assistance, and working memory capacity in a complex networked environment.

Delegating to Automation Performance, Complacency and Bias Effects under Non-Optimal Conditions

We have advocated adaptable automation approaches—those in which the human retains the role of instructing and tasking—and specifically have used the metaphor of a sports team’s “playbook”. Several prior experiments have shown benefits to flexible play calling, so the present work focuses on performance in “non-optimal play environments” (NOPEs) where the defined plays are a poor fit resulting in a need to either modify them dynamically (provide additional instruction) or to abandon play-level automation and resort to more manual levels of control. We might expect that prolonged play usage under optimal conditions would result in automation complacency effects and even loss of training. In two reported experiments, we find little evidence for complacency effects and, instead, show that having access to plays sometimes provides benefits even during NOPE intervals where they were not (directly) useful.

Adaptive Automation to Improve Human Performance in Supervision of Multiple Uninhabited Aerial Vehicles Individual Markers of Performance

Adaptive automation has been shown to offer flexible, context-dependent, and user-specific automation that can enhance human-system performance. While several invocation methods for adaptive automation have been proposed and tested in experimental settings, it is not clear which of these methods can practically be implemented in operational environments. It is therefore important to explore measures that are both predictive of individual performance and that can be easily administered in actual work environments. This study examined the efficacy of using both baseline manual performance and working memory capacity to predict future performance with automation. Participants were assisted by context-dependent adaptive automation during a simulated command and control task. Results showed that baseline performance without automation predicted overall human-automation performance. Working memory capacity did not predict overall performance, but did predict effective use of the automated aids, so that participants with higher working memory scores used the aids more effectively. These results suggest that effectiveness of human-automation teams can be predicted with quick, cost-efficient, easily measureable markers of performance and can therefore provide practical invocation strategies for adaptive automation.

Using Transcranial Doppler Sonography to Measure Cognitive Load in a Command and Control Task

Previous work has explored the possibility of measuring the functional state of the operator to drive the implementation of physiological adaptive aiding. Transcranial Doppler Sonography (TCD), which has shown promise as an index of cognitive resource utilization in vigilance or sustained attention tasks, may provide a time and cost efficient alternative to traditional measures used to assess operator functional state. In the current study, participants performed a command and control simulation under varying levels of task load: a low task load condition in which enemy threats incurred at a steady pace, and a high workload trial in which the number of enemy threats increased unpredictably at two points within the scenario. Reaction time to engage and destroy enemies, and the efficiency of protection of a no-fly zone, were superior in the low than in the high load condition. Furthermore, an automated decision aid facilitated better performance in both task load conditions. As the demands of the task increased unpredictably in the high task load condition, cerebral blood flow velocity (CBFV) increased in a similar manner for the first task load transition, but not for the second. Results suggest that the TCD measure may be useful in monitoring the dynamic changes of operator workload in unpredictable environments, but additional studies are needed to validate its use for physiologically-driven adaptive automation.

Team Performance and Communication within Networked Supervisory Control Human-Machine Systems

The effects of task load, automation reliability and team communication on supervisory control performance were examined using a multi-UAV simulation with two operators. Performance was degraded by high task load and improved with an automated decision aid. In addition, team working memory, defined as the average of individual working memory capacity scores, was associated with superior team performance. Higher levels of task load increased the amount of information communicated by teams whereas the presence of an automated decision aid decreased the amount of information communicated by teams. The results are discussed in relation to models of team cognition for teams performing similar tasks in a shared, networked human-machine system.

Effects of Imperfect Automation and Task Load on Human Supervision of Multiple Uninhabited Vehicles

Many current and emerging systems require human operators to supervise multiple uninhabited vehicles (UVs) with the support of automation. Automation is not 100% reliable; ergo it is important to understand the effects of automation imperfection on performance. This study investigated the effects of automation reliability on system performance with multiple UVs under different levels of task load. Twelve participants completed 12 “missions” supervising 3 (low load) or 6 (high load) UVs. Participants used one UV to conduct Reconnaissance, Surveillance and Target Acquisition. They were assisted with an automatic target recognition (ATR) system whose reliability was low, medium, or high. Overall system performance was higher than user or ATR performance alone. The gain in system performance with the ATR was particularly effective with medium and high automation reliability. Thus, human-robot teams can benefit from imperfect automation even under high workload conditions.

Individual performance markers and working memory predict supervisory control proficiency and effective use of adaptive automation

Adapting automation to transient operator states and changes in the environment has been shown to be more effective than static automation. Adaptive automation design that incorporates individual human operator differences can further enhance human-automation interaction. In this study participants performed a simulated air defence task under low and high task load and three levels of automation (manual, low and high). Automated aids autonomously engaged targets and communicated actions via a text messaging system. Baseline performance measures not only predicted future performance but also predicted use of automation. Operators with high skill proficiency exhibited greater disuse of automation compared to their lower skill counterparts. Contrary to previous findings, working memory spans did not predict overall performance, but did predict appropriate use of automation in non-context matched scenarios. When automation was not matched to level of task load, operators with higher spans were better able to coordinate with automation than lower span individuals.

Evaluating Situation Awareness in Human-Robot Teams

New methodologies and quantitative measurements for evaluating human-robot team performance must be developed to achieve effective coordination between teams of humans and unmanned vehicles. The Mixed Initiative Team Performance Assessment System (MITPAS) provides such a comprehensive measurement methodology. MITPAS consists of a methodology, tools and procedures to measure the performance of mixed manned and unmanned teams in both training and real world operational environments. This paper shows results of an initial experiment conducted to validate the Situation Awareness Global Assessment Technique (SAGAT) methodology as part of the MITPAS tool and gain insight into the effect of robot competence on operator situation awareness as well as on overall human-robot team performance.