Joseph Coyne

Applying Real Time Physiological Measures of Cognitive Load to Improve Training

This paper discusses how the fields of augmented cognition and neuroergonomics can be expanded into training. Several classification algorithms based upon EEG data and occular data are discussed in terms of their ability to classify operator state in real time. These indices have been shown to enhance operator performance within adaptive automation paradigms. Learning is different from performing a task that one is familiar with. According to cognitive load theory (CLT), learning is essentially the act of organizing information from working memory into long term memory. However, our working memory system has a bottleneck in this process, such that when training exceeds working memory capacity, learning is hindered. This paper discusses how CLT can be combined with multiple resource theory to create a model of adaptive training. This new paradigm hypothesizes that a system that can monitor working memory capacity in real time and adjust training difficulty can improve learning.

Augmented Reality for Urban Skills Training

Warfighters develop and maintain their skills through training. Since fully-manned live training in the real world is often too expensive (by many measures), scientists have developed many types of training systems ranging from classroom sessions to those using virtual reality. Recently, researchers have used augmented reality (AR) to insert virtual entities into the real world, attempting to create a low cost, repeatable, and effective substitute for fully-manned live training. However, very little evaluation of the effectiveness of AR for training has been performed. We performed a pilot study to evaluate the use of wearable AR in teaching urban skills, specifically, room clearing in teams. Eight teams of two were briefed on room clearing techniques, given hands-on instruction, and then allowed to practice those techniques with or without the AR system. After this instructional period, subjects performed several room clearing scenarios against real people using infrared-based practice weapons that logged the number of hits on the subjects and the enemy and neutral forces. During these trials, a subject matter expert evaluated how well the subjects applied the room-clearing techniques. In this paper, we describe the pilot study in more detail, including the hardware and software testbed, and then provide an analysis of the results of the pilot study.

Investigating the Use of Two Low Cost Eye Tracking Systems for Detecting Pupillary Response to Changes in Mental Workload

Eye tracking technologies are being utilized at increasing rates within industry and research due to the very recent availability of low cost systems. This paper presents results from a study assessing two eye tracking systems, Gazepoint GP3 and Eye Tribe, both of which are available for under

The Human Factor in Cybersecurity: Robust & Intelligent Defense

500 and provide streaming gaze and pupil size data. The emphasis of this research was in evaluating the ability of these eye trackers to identify changes in pupil size which occur as a function of variations in lighting conditions as well as those associated with workload. Ten volunteers participated in an experiment in which a digit span task was employed to manipulate workload as user’s fixated on a monitor which varied in background luminance (black, gray and white). Results revealed that both systems were able to significantly differentiate pupil size differences in high and low workload trials and changes due to the monitor’s luminance. These findings are exceedingly promising for human factors researchers, as they open up the opportunity to augment studies with non-obtrusive, streaming measures of mental workload with technologies available for as little as

Mental Workload as a Function of Road Type and Visibility: Comparison of Neurophysiological, Behavioral, and Subjective Indices:

100.

Demonstrating the Supervisory Control Operations User Testbed (SCOUT)

In this chapter, we review the pervasiveness of cyber threats and the roles of both attackers and cyber users (i.e. the targets of the attackers); the lack of awareness of cyber-threats by users; the complexity of the new cyber environment, including cyber risks; engineering approaches and tools to mitigate cyber threats; and current research to identify proactive steps that users and groups can take to reduce cyber-threats. In addition, we review the research needed on the psychology of users that poses risks to users from cyber-attacks. For the latter, we review the available theory at the individual and group levels that may help individual users, groups and organizations take actions against cyber threats. We end with future research needs and conclusions. In our discussion, we first agreed that cyber threats are making cyber environments more complex and uncomfortable for average users; second, we concluded that various factors are important (e.g., timely actions are often necessary in cyber space to counter the threats of the attacks that commonly occur at internet speeds, but also the ‘slow and low’ attacks that are difficult to detect, threats that occur only after pre-specified conditions have been satisfied that trigger an unsuspecting attack). Third, we concluded that advanced persistent threats (APTs) pose a risk to users but also to national security (viz., the persistent threats posed by other Nations). Fourth, we contend that using “red” teams to search cyber defenses for vulnerabilities encourages users and organizations to better defend themselves. Fifth, the current state of theory leaves many questions unanswered that researchers must pursue to mitigate or neutralize present and future threats. Lastly, we agree with the literature that cyber space has had a dramatic impact on American life and that the cyber domain is a breeding ground for disorder. However, we also believe that actions by users and researchers can be taken to stay safe and ahead of existing and future threats.

Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations

Advanced in-vehicle technologies (IVTs) are rapidly being introduced. Methods of assessing the mental workload required by the driving situation are imperative to the safe implementation of these advanced systems. The current investigation compared the sensitivity of an array of assessment techniques to changes in simulated driving task demand. P300 amplitude was sensitive to increased driving task demand due to reduced visibility (presence of fog) but was not sensitive to changes in road type (urban versus freeway). Conversely, RT and accuracy to a secondary task were sensitive to changes in road type but were not sensitive to the visibility manipulation. Subjective workload ratings were not sensitive to either manipulation. Results are discussed in terms of their implications for assessing the impact of environmental factors on the mental workload demands of the driving task and for the design and implementation of adaptive driver interfaces.

Proactive decision support for dynamic assignment and routing of Unmanned Aerial Systems

The proliferation of unmanned systems and the large crew compliment involved in operating a single UAV has created a manpower problem in the U.S. military and resulted in a desire to enable supervisory control of future systems. The Supervisory Control Operations User Testbed (SCOUTTM) is a flexible research testbed developed by the Naval Research Laboratory (NRL) to investigate human performance and automation challenges involved in realizing this future supervisory control vision. As such, SCOUT was designed to with input from UAV operators to increase ecological validity and represent the complexity, noise and uncertainty associated with unmanned system control. SCOUT enables the investigation of various psychological phenomena, including decision making (e.g., route planning under uncertainty), attention allocation (e.g., where and when to focus effort and attention), and mission monitoring, (e.g., maintaining vigilance). Furthermore, SCOUT incorporates eye tracking and physiological monitoring to provide metrics of operator state when performance metrics are limited, which is often the case in real world operations when the operator is only passively monitoring the system. This paper and its associated demonstration will provide an overview of the various components of SCOUT and aims to encourage other researchers to take advantage of this free software for conducting their own experimentation.

Automated SAF adaptation tool (ASAT)

Greater unmanned system autonomy will lead to improvements in mission outcomes, survivability and safety. However, an increase in platform autonomy increases system complexity. For example, flexible autonomous platforms deployed in a range of environments place a burden on humans to understand evolving behaviors. More importantly, when problems arise within complex systems, they need to be managed without increasing operator workload. A supervisory control paradigm can reduce workload and allow a single human to manage multiple autonomous platforms. However, this requires consideration of the human as an integrated part of the overall system, not just as a central controller. This paradigm can benefit from novel and intuitive techniques that isolate and predict anomalous situations or state trajectories within complex autonomous systems in terms of mission context to allow efficient management of aberrant behavior. This information will provide the user with improved feedback about system behavior, which will in turn lead to more relevant and effective prescriptions for interaction, particularly during emergency procedures. This, in turn, will enable proper trust calibration. We also argue that by understanding the context of the user’s decisions or system’s actions (seamless integration of the human), the autonomous platform can provide more appropriate information to the user.

Supporting Multi-objective Decision Making Within a Supervisory Control Environment

Unmanned Aerial System (UAS) missions are executed by teams of operators with highly specialized training and roles; however, the task demands on each operator are highly variable, often resulting in uneven workloads among operators and sometimes in mishaps. Therefore, there is a need to develop anticipative and effective decision support algorithms that permit the evaluation of courses of action (COAs), while assuring that operators are attending to the right task at the right time and that task demands do not exceed the operators cognitive capabilities in dynamic multi-mission environments. Motivated by the need to assist UAS operators in efficiently managing their workloads, this paper develops algorithms for the dynamic scheduling of UAS tasks by providing efficient COA recommendations in an unobtrusive manner. The dynamic scheduling of a set of UASs to search for targets with varying rewards is an NP-hard problem. We model this problem as an extension to the open vehicle routing problem (OVRP). Extensions to OVRP include risk propensity of human decision making, task deadlines, and multiple vehicle types. UAS operators would benefit greatly from the COA recommendations and the algorithms proposed in this paper by (a) enhancing rapid planning and re-planning capabilities; (b) proactive allocation of UASs, while balancing operator workloads; and (c) adapting plans as new targets of opportunity appear or information is updated about a target and/or UAS. The proposed algorithms are embedded in the Supervisory Control Operations User Testbed (SCOUTTM), an experimental paradigm developed by the Naval Research Laboratory-Washington DC.