2019-2020 Assessment of the Army Research Laboratory (2021) / Chapter Skim
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4 Human Sciences
4 Human Sciences
Pages 41-60
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From page 41... ...
The HS project areas reviewed were human-autonomy team interactions and humans understanding autonomy; autonomy understanding humans and estimating human-autonomy team outcomes; human interest detection; cyber science and kinesiology; neuroscience, training effectiveness, and strengthening teamwork for robust operations with novel groups (STRONG)
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From page 42... ...
Key problems are being explored, including autonomy adapting to humans, autonomy understanding humans, trust, spatial mental models, and estimating team outcomes. ARL has identified expertise gaps in the composition of their research team and has a plan in place to enhance internal expertise while connecting with the broader community via the STRONG program.
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From page 43... ...
Continuing to Build In-House Expertise in Team Science There is an opportunity to continue to build team science expertise in house. In order to successfully study human-autonomy teaming and provide cutting-edge applications, it is necessary to have a deep knowledge of traditional and nontraditional teaming approaches.
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From page 44... ...
It is expected that the STRONG program will help bolster external team science expertise. Directing and Using Unique Access for Human-Autonomy Teaming To achieve its stated goals, ARL is in an advantageous position with unique access to the U.S.
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From page 45... ...
leverage ML techniques in sensor fusion for human understanding. These projects include convolutional neural networks used to measure human performance through prediction of p300 variability,4 soldier gaze tracking and neural activity used to passively detect salient environment features at a team level, and methods for understanding the quality of human communications tools for annotating human conversations.
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From page 46... ...
Human motion is captured during walking or running and can be replayed on a hexapod for simulating human walking motions, which allows for simulation of real-world noise and the study of the motion artifact. The project on human-AI interactions for intelligent squad weapons seeks to use advanced sensing algorithms to perform automated target recognition.
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From page 47... ...
The research project on p300-passive detection of situation awareness for the battlefield environment is a good opportunity for the HS core competency area. The team demonstrates the relevant expertise to attack the problem and understanding of the limitations of current methods (e.g., task specificity, subject specificity, noise, and requirements for large data sets)
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From page 48... ...
The program would benefit by management direction to emphasize human science-focused problems, while better matching open source ML or engineering approaches to the specific research problems. Although this issue was primarily observed in one project, all projects that leverage black box ML techniques or those that require novel ML techniques could create a distraction around the existing dedicated focus on human sciences research.
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From page 49... ...
, overcoming motion artifacts in the data, and the use of adaptive decision fusion for decision making. There are several projects in other programs that are not officially under the umbrella of HID but could be included in the exploration of this area.
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From page 50... ...
Quality behavioral measures and metrics are foundational to advancing scientific progress in this field, and the cyber team is making important contributions in developing this science at the individual level and at the team level. It is not clear how this work could support ARL's objective of supporting cyber operational planning.
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From page 51... ...
Of particular note is the project on team performance in a series of cybersecurity defense competitions: generalizable effects of training-type and functional role specialization, with the team actively engaging with collegiate cyber teams and collecting data. The active, deep engagement of collegiate cyber teams, as a source of input data and as test subjects for the results of other projects, is particularly creative given their skill sets and the availability challenges of operational cyber protection teams (CPTs)
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From page 52... ...
There are three important scientific purposes that this work might address: machine modeling as it relates to brain areas, contributions to cognitive theory as it relates to joint actions between pairs of team members (human-human or human-robot pairs) and to integration of perception with action in predictive processing, and archiving the nearly unique set of individual and team performance data that this work is and will be generating.
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From page 53... ...
Advancing Cognitive Theory A side effect of ignoring the cognitive modeling community may be the lack of awareness of changes in areas of research developed over the past 15 years as well as the concomitant changes in basic cognitive theory. At least two of these changes are directly relevant to ongoing HS research -- joint action and predictive processing.
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From page 54... ...
Well beyond fish and arms, joint action focuses on nondeliberate actions such as the coordination of eye movements when two humans work as a team on a physical task wherein the nonverbal, point-ofgaze of one human attracts the point-of-gaze of the other human without deliberate thought or conscious awareness by either team member. A critical review of the emerging action simulation theories in the wide-ranging embodied cognition and motor cognition literatures provides an integrative neurocomputational account of action simulation that links it to the neural substrate and to the components of a computational architecture that includes internal modeling, action monitoring, and inhibition mechanisms.18 A second and related omission is predictive processing (PP)
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From page 55... ...
Autonomy Understanding Humans and Estimating Human-Autonomy Team Outcomes Many researchers demonstrate an in-depth understanding of the unique challenges in their research. Researchers on the brain dynamics of driver-passenger communication, human-AI interactions for intelligent squad weapons, and trust in automation efforts demonstrate a broad understanding of the state of the art.
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From page 56... ...
. The work has not yet characterized fully the quality of binary target detection (the trade-off between probability of detection versus probability of false alarm and the development of the receiver operating characteristic [ROC]
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From page 57... ...
ARL should determine the appropriate mechanism for conducting research that leverages the state of the art in both human sciences research and machine learning research. Human-Autonomy Team Interactions and Humans Understanding Autonomy ARL needs to continue to collaborate with universities through its state-of-art data collection facilities and through the STRONG program.
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From page 58... ...
The continued success of ARL and the human sciences core competency is critical to ensuring that human-autonomy teaming does not end up being solely technology centered. Understanding the communications and interactions between human-machine agent teaming is critical for the operational success of teams and system performance.
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From page 59... ...
Recommendation: The kinesiology researchers should consider the top-level question of what is different about kinematics for the Army rather than being pulled solely by ARL's strong focus on human-robot teaming. Neuroscience, Training Effectiveness, and STRONG Changes made in the personnel, organization, and structure of the HS core competency area have provided the tools and opportunities needed to make it a national laboratory in the area of cognitive science and robotics for individual human performance as well as for human teams and mixed teams of humans and robots.
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From page 60... ...
should adopt a research strategy that embraces the findings of predictive processing, with particular attention to the dynamics of military operations.
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