COGNITIVE SIMULATION

Researcher: [Michael Freed](../../people/mike.html)

New procedures and equipment sometimes make excessive demands on the
mental and physical capacities of human operators. For complex, dynamic
tasks such as air traffic control, the only really good way to evaluate
new procedures and equipment is to run simulation studies using
equipment prototypes and hired controllers. This is very expensive. One
way to get some of the benefits at much lower cost is to use a computer
to simulate all elements of such a study including the equipment, human
operators, and experimental observers.

Human simulation has been used successfully to guide design. However,
past approaches have simulated relatively simple tasks in which
performance variability arises from people\'s physical rather than
cognitive limitations. In contrast, our approach focuses on limitations
of human memory and attention that lead to error.

[Example](../cogsim1.html)

We are developing a computer program intended to partially automate the
process of predicting excessive operator memory requirements in new
procedure and equipment designs. The system includes components for
simulating a physical operating environment, a human operator, and an
experimental observer.

The simulation of the physical environment is specialized for the domain
of TRACON air traffic control. This system component includes models of
a radar scope and other equipment that a user may be interested in
evaluating.

The human operator model describes domain-independent sources of
performance variability including perceptual limitations (e.g. acuity,
field-of-view, discriminability) and motor limitations (Fitt\'s law,
limited hand-independence). The centerpiece of the model describes plan
execution mechanisms which enable the simulated agent to carry out
complex tasks with an approximately human proneness to habit captures
and other systematic forms of error.

This element of the model is intended to be significant in two ways.
First, other simulations of human operators are incapable of operating
in domains as complex as air traffic control. These models are therefore
unable to determine whether designs violate even straightforward
constraints \-\-- e.g. insuring that an operator doesn\'t have to look
at two widely separated objects at the same time \-\-- because there is
no way of determining whether the agent would ever get into the
proscribed situation. Our operator model implements the capabilities of
a \"sketchy planner\" \[Firby 1989\] and is thus capable of interleaving
task execution, gradually specifying plans based on intentionally
acquired information, and other relatively complex activities.

Second, most other systems neglect to consider the role of cognitive
factors in performance, especially factors associated with executing
tasks in complex, dynamic environments. This unnecessarily limits the
usefulness of such systems for predicting design-facilitated operator
error since many of the most important kinds of operator error can be
predicted (in a statistical sense) if cognitive factors are taken into
account.

In addition to components for simulating a physical environment and
human operator, the system simulates an observer charged with noticing
anomalies in the operator\'s performance in order to provide guidance to
a designer. This part of the system consists of a component for
monitoring deviations from expected performance and a component for
explaining and classifying the expectation failure using
model-based-reasoning techniques. The need to automate this task follows
from the vast amount of data generated over the course of a simulation,
although human observers of human subjects are faced with a similar,
immensely time-consuming task.

In air traffic control, and in many other domains, design decisions are
constrained not only by human factors and physics, but by materials
costs, operator retraining costs, compatibility with past equipment and
procedures, and any number of political factors. Reports on prototype
simulation studies usually describe observed problems with a design, but
do not attempt to prescribe specific solutions. Similarly, our system
outputs problems experienced by a simulated operator while using
specified equipment and procedures, leaving the decision about how to
cope with these problems in the hands of the user.

For More Information: send inquiries to <mfreed@mail.arc.nasa.gov>

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