2019-2020 Assessment of the Army Research Laboratory Interim Report (2020) / Chapter Skim
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4 Human Sciences
4 Human Sciences
Pages 38-57
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From page 38... ...
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 39... ...
ARL is focused on performing basic research that contributes to what is referred to by ARL leadership as "foundational" research -- described as the integration of both basic and applied work. This work encompasses both sides of the human-autonomy teaming paradigm, autonomy understanding humans and humans understanding autonomy.
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From page 40... ...
These operational environments are a platform for providing real-world context and evaluation of human-autonomy teaming initiatives. Continuing to Build In-House Expertise in Team Science There is an opportunity to continue to build team science expertise in house.
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From page 41... ...
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 42... ...
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 43... ...
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 44... ...
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 45... ...
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 46... ...
, 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 47... ...
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 48... ...
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 49... ...
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 50... ...
Advancing Cognitive Theory A side effect of ignoring the cognitive modeling community may be the lack of awareness for 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 51... ...
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 52... ...
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 53... ...
. 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 54... ...
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 55... ...
that results in a better understanding of human-autonomy interactions. Autonomy Understanding Humans and Estimating Human-Autonomy Team Outcomes Many researchers demonstrate an in-depth understanding of the unique challenges in their research.
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From page 56... ...
Recommendation: The human sciences core competency area should build on its strengths by acquiring expertise in those areas of cognitive science that focus on joint actions among individual intelligent entities (i.e., human-human and human-robot)
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From page 57... ...
Research Laboratory (ARL) should adopt a research strategy that embraces the findings of predictive processing, with particular attention to the dynamics of military operations, which would enable the researchers to join the ranks of national laboratories that advance science through their engineering as well as their basic research contributions to society.
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