Ciara Sibley

Quantification of Contrast Sensitivity and Color Perception using Head-worn Augmented Reality Displays

Augmented reality (AR) displays often reduce the visual capabilities of the user. This reduction can be measured both objectively and through user studies. We acquired objective measurements with a color meter and conducted two user studies for each of two key measurements. First was the combined effect of resolution and display contrast, which equate to the visual acuity and apparent brightness. The combined effect may be captured by the contrast sensitivity function and measured through analogs of optometric exams. We expanded the number of commercial devices tested in previous studies, including higher resolution and video-overlay AR displays. We found patterns of reduced contrast sensitivity similar to previous work; however, we saw that all displays enabled users to achieve the maximum possible acuity with at least moderate levels of contrast. The second measurement was the perception of color. Objective measurements showed a distortion of color, notably in the blue region of color space. We devised a color matching task to quantify the distortion of color perception, finding that the displays themselves were poor at showing colors in the blue region of color space and that the perceptual distortion of such colors was even greater than the objective distortion. We noted significantly different distortions and variability between displays.

Basic Perception in Head-Worn Augmented Reality Displays

Head-worn displays have been an integral part of augmented reality since the inception of the field. However, due to numerous difficulties with designing using such unique hardware, the perceptual capabilities of users suffer when looking at either the virtual or real portions of the augmented reality.We discuss the perceptual background and a series of experiments – in the literature and in our laboratories – measuring the degradation of basic functions of the human visual system when using head-worn augmented reality displays. In particular, we look at loss of visual acuity and contrast (and how these in turn affect text legibility), distortion of perceived colors, and difficulties of fusing stereo imagery. We discuss the findings and the implications for head-worn display design.

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

Demonstrating the Supervisory Control Operations User Testbed (SCOUT)

100.

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

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.

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.

Supporting Multi-objective Decision Making Within a Supervisory Control Environment

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.

Pupil size as a measure of within‐task learning

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.

On the use of hidden Markov models for gaze pattern modeling

This paper discusses decision making challenges involved in the management of multiple unmanned vehicles within a dynamic mission environment. Given the increased likelihood of this new supervisory control paradigm, the authors developed the Supervisory Control Operations User Testbed SCOUT. A brief overview of SCOUT will be provided, followed by a summary of initial research conducted within the testbed which demonstrates how eye tracking measurements can be utilized to assess workload and predict situation awareness. Subsequent discussion will address challenges associated with dynamic decision making under uncertainty, with respect to multiple asset allocation. Techniques for measuring the accuracy of these decisions as well as assessing operator risk throughout the mission will also be presented. The paper concludes with discussion of how these new decision making metrics can be used to drive decision aids and compares decision making performance and risk bias under varying levels of task load.