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Investigating Compensatory and Noncompensatory Decision Making
Strategies in Dynamic Task Environments
Sponsor: National Science Foundation (SES-0452416)
In the modern, evolving workplace there exists a need to support
workers in adapting to novel demands and opportunities. Traditionally,
human judgments are characterized by weighted strategies where
tradeoffs between decision criteria are made. For example, college
admissions committees consider multiple facets of an applicant
and, often, a poor grade point average can be compensated by
a superior entrance exam score. However, on occasions in which
time stress or high workload exists, human judgments are typically
rule-based where factors are not weighted. For instance, an
air traffic controller is likely to diagnose potential problems
in high workload situations based on a few salient cues.
This research will add to the growing body of knowledge concerning
the way in which the decision environment influences the strategy
used by decision makers. While characterizations such as weighted
and rule-based strategies have been deduced from a multitude
of experimental studies, no framework exists to model and predict
shifts in judgment strategy in individual decision makers. The
central theme of this research is to create a mathematical framework
to model the shift from a weighted to a rule-based strategy,
and vice versa, in work environments where workload and stress
levels vary. This research will build upon existing work to
create a model which infers judgment rules from human data.
Multiple experiments will be conducted to compare the performances
of the rule-based model with a commonly-used weighted model
under varying workload and time stress situations. It is hypothesized
that increasing workload and time stress will promote a systematic
shift from weighted to rule-based strategies. This work will
make a valuable contribution toward understanding decision making
in complex, dynamic environments. One potential implication
of this research is the design of an aiding mechanism that adapts
to the needs of the user based on conditions of dynamic work
environments.
The Effects of
Framing on Dynamic Multi-Criteria Decision Making in Future
Combat Systems
Sponsor:
Micro Analysis and Design, Inc.
The
purpose of this research is to investigate the consistency of
human supervisory controllers as the decision task changes from
a static to dynamic context. This is particularly important
because neither the training nor the doctrine for Future Combat
Systems (FCS) follow empirical guidance in dynamic decision
contexts. Our lab has designed a model and simulation of multi-criteria
decision making to better understand Pareto solutions to this
problem. The research will continue to use empirical and theoretical
understanding to create new visualization concepts for dynamic
threat assessment.
Team
Aegis Simulation Platform (TASP)
Sponsor:
U.S. Navy
The
purpose of this project is to extend an existing suite of tools
known as the Wright State Aegis Simulation Platform (WASP) to
better enable researchers to assess and model human performance
in a team environment.
This project will produce a team human-in-the-loop simulation
testbed called the Team Aegis Simulation Platform (TASP) to
support modeling efforts to improve training effectiveness.
The proposed effort will extend WASP by adding more tasks and
responsibilities in a three-person team configuration, incorporate
hooks for software models of each role to be able to interact
with the simulation in real time, incorporate hooks for diagnostic
algorithms and feedback algorithms, enable automatic collection
of performance data to include automatic speech recognition,
develop diagnostic algorithms consistent with the Team Dimensional
Training methodology, design and develop software agents to
identify and measure team time windows.
For more information on TASP, click here.
Wright
State Aegis Simulation Platform (WASP)
Sponsor: U.S. Navy
Today's battle space consists of vastly complex and dynamic
environments within which human decision makers must adapt in
order to achieve their objectives. Moreover, the recent trend
of manpower reductions necessitates the implementation of highly
automated systems. Therefore, a decision maker must not only
function under conditions of target ambiguity, time pressure,
and information overload, he/she must also be able to effectively
operate somewhat autonomous pieces of machinery. To support
service members who must work in these environments, researchers
should first seek to understand fundamental decision making
processes in the context of situations that are dynamic, complex,
and highly automated.
GT-ASP is a low fidelity Naval Air Defense Warfare simulator
developed by Georgia Institute of Technology for the Naval Air
Warfare Center Training Systems Division (NAWCTSD). Over the
past five years, the Navy has used GT-ASP in many training investigations,
but the current state of technology has precluded the use of
the original program. Although the proposed principal investigator
has informally supported the use of the software by providing
free technical assistance over the past five years, the simulation
suite has become a legacy system that is no longer cost-effective
to supporting rigorous scientific research. The present effort
is intended to update GT-ASP to be more flexible, run on current
platforms, allow the experimenter more experimental control,
and add the capability to run the simulation as a team task.
Wright State Aegis Simulation Platform (WASP) is a result of
our extension of GT-ASP.
For more information on WASP, click here.
Adaptive
Aiding Using Physiological Operator Functional State Assessment
Sponsor: Dayton Area Graduate Studies Institute Grant, Ohio
Board of Regents
Our project goal is to
develop a framework in which physiological state estimation
methods are coupled with techniques to assess dynamic task demands
to determine the adaptive aiding required for effective task
accomplishment in a UCAV simulator. To systematically
achieve our goal, we have established six objectives that further
describe the phases of our project. The objectives are:
(1) UCAV environment development;
(2) development of psychophysiological
state estimation algorithms;
(3) physiological state
model development;
(4) adaptive interface design
and construction;
(5) system integration;
and
(6) system evaluation and
technology transfer.
The
project synthesizes the collective expertise from three complementary
DAGSI efforts worth over $1 million in research costs. If successful,
this project has the potential to integrate the results of two
existing DAGSI research projects to build adaptive aiding mechanisms
based on physiological models of human operators in a complex
task environment. We are teamed with Battelle, a world-wide
leader in the development of high-quality solutions and products
to industry and government clients that is headquartered in
Columbus to handle and facilitate the technology transition.
For more information, click here
LEGOŽ
MindStorm-based Configurable Telerobotics System (LMCTS)
Sponsor: Wright State University, College of Engineering
LMCTS
stands for "LegoŽ Mindstorm-based
Configurable Telerobotics System" laboratoy. LMCTS presents
a methodology to analyze and model human supervisory control
performance in a highly configurable telerobotics system. The
system consists of a semi-autonomous mobile robot, a scalable
task environment, and a configurable user interface.
Researchers, at LMCTS examined the effects of different tasks
and task environments on human performance through testing in
a graduate-level course to instruct cognitive modeling techniques.
Specifically, we describe five interactive tasks in which students
were required to successfully control the telerobotics system.
Models of user performance on each of the tasks were developed
by class members. Initial modeling and performance assessment
results are reported as part of the work in progress to demonstrate
the potential of the research program to assess and model single-user
as well as collaborative supervisory control systems.
For more information on
LMCTS lab, click here
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