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OCR for page 1
1
Introduction
SCOPE
This report discusses human performance models (HPMs) and their
potential use in system design, development, and evaluation. The primary
focus is modeling system operators performing supervisory and manual
control tasks. The report does not address models of the designer or
manager of a complex system, and it addresses models of maintainers only
briefly (see Unkind, Card, Hochberg, and Huey, 1989, for a discussion of
models pertinent to designers and managers). However, if a model cannot
be understood by higher management, it is not likely to be used by them.
Of interest are complex technological systems of a dynamic nature
in which humans play a central role in any of the functions: monitoring,
control, decision making, supervision, and maintenance. Examples include
vehicles (air, sea, or land), process control operations, power plants, some
weapons systems, and a variety of manufacturing systems. Such systems are
invariably costly and time-consuming to design and develop, and substantial
risks are often involved ~ their operation. Faulty design or operation can
be very expensive or dangerous, and systematic means of accounting for
the performance of the human component in these systems is imperative.
A model is a representation or description of all or part of an object or
process. A varietr of models have been developed for a variety of reasons.
Early models, which were often verbal, statistical, or mathematical descrip-
tions or theories of some limited aspect of human performance, could not
represent the complexity and comprehensiveness of human performance.
However, modern computer technology is changing this situation. Until
fairly recently, most human performance models were numerical or quan~i-
tative, but as a result of the progress in artificial intelligence and cognitive
1
OCR for page 2
2
QUANTITATrVE MODELING OF HUMAN PERFORMANCE
science, a substantial body of nonnumerical, qualitative, but calculable,
models has been developed. These models are necessary for representing
cognitive behavior and, although qualitative, are nevertheless computa-
tional.
Although the literature is replete with models that represent paradigms
and tasks in which an individual's attention is fully committed to a single
process, the challenge addressed here is to represent human performance
in typical working settings in which operators perform a collection of tasks
that overlap in time. For example, the submarine commander is engaged In
navigation, control, and threat detection. At various times, these activities
compete for attention. This added level of complexity poses important
problems in modeling human performance. In addition to models that are
appropriate for single tasks or activities, it is necessary to model the ways
in which human operators manage their own resources so as to cope with
the changing and sometimes conflicting demands of disparate activities.
A major question that arises is: Can this be accomplished by integrating
single-task models that have been developed previously for the activities
performed in isolation, or is it necessary or better to model the complex
task in a completely unified manner?
The extent to which simple task models can be usefully integrated
to represent more comprehensive behavior depends on the nature of the
gaps in coverage of the models and on the completeness of the linkages
between them. A report by ELkind et al. (1989) addresses this issue in the
visual and cognitive areas win specific reference to the tasks of a helicopter
pilot. On the other hand, most Busting comprehensive models contain little
detail about specific aspects of human performance, reflecting the trade-off
between breadth and depth Therefore, at present, some trade-off decisions
must still be made.
It should be noted that human performance modeling has additional
purposes and uses beyond those of prime consideration here. Of special
interest and import is the use of models in theory development and evalua-
tion. Indeed, in the psychological literature a model of human performance
is often used as a synonym for a theory of performance. In the psycholog-
ical literature the model frequently is, or is intended to be, independent
of the specific system or task context and thus is applicable to a varied
of systems. This is an undoubtedly important area of human performance
modeling, but it is not of central interest in this report.
WEIAT IS HUMAN PERFORMANCE MODELING?
The term human performance models, as used in this report, refers to
quantitative (analytic or computer-based) models of human operators or
maintainers of complex dynamic systems. Many different kinds of HPMs
OCR for page 3
INTRODUCTION
3
have been developed. The characteristics that help distinguish among them
can be represented along several important dimensions: output versus
process orientation, predictive versus descriptive, prescriptive (normative)
versus descriptive, top-down versus bottom-up, and single-task versus mul-
titask Models can also be characterized according to the types of theories
or tools used in their development.
Output Versus Process
The dimension of output versus process relates to the degree to which
a model (or modeling approach) focuses the system output versus the
processes by which output is generated. An output model is a set of rela-
tionships between input and output states that is capable of (1) beginning
with input states and (2) generating output states. This type of model pre-
dicts or describes the outputs of a person or a person-machine system for a
given set of inputs. Such an output-oriented model places no requirement
on the structure, or even the validity, of the internal mechanism (processes)
of the model. All that is desired is that the model produce "correct" (i.e.,
useful in the context of the application) outputs for specified inputs.
On the other hand, a model can be a theory of how people perform
certain tasks. The HPMs with this characteristic describe processes by
which an output is generated and, as such, describe what humans do within
the system, rather than just predicting the results of their actions. In this
sense, process models are more complete descriptions than are output
prediction models. For many purposes, though, output prediction is all
that Is needed.
Human performance models typically combine output prediction with
some degree of process prescription. No general answer can be given
to the question, What is the "appropriate" level of internal detail for an
HPM? because the necessary level of process description depends on the
application of the model.
Predictive Versus Descriptive
It is important to distinguish between two distinct methods of em-
ploying HPMs: (1) predicting human-system performance with the model
prior to collection of data and (2) describing (fitting the model to) human-
system performance by adjusting free parameters of the model to conform
to existing data.
Fitting models to data can be an end in itself (i.e., for descriptive
purposes). It can also be a step toward developing predictive models.
Virtually all HPMs have some parameters that must be estimated from
OCR for page 4
4
QUAN7CITATI~E MODELING OF [IUMAN PERFORMANCE
experimental data. Predictions can be made for new situations by using the
parameter estimates available from earlier, descriptive studies.
Clearly, predictive models, where they exist or can be developed, are
intrinsically of more value than models that merely describe or summarize
data; prediction Is the real need of the system designer (prior to building
the actual system). Moreover, a truly predictive model will also describe
actual performance.
Prescriptive (Nonnative) Versus Descriptive
Models for human performance can either describe how a human
is likely to perform a task or predict ideal behavior, given human and
situational limitations. In the former case the model is called descriptive,
whereas the latter type of model, which preaches how the human should
perform if he were to behave in a rational way that takes into account
the information available, the constraints that exist, the risks, rewards, and
objectives, is called prescriptive or normative.
The distinction between normative and descriptive can be blurred
because prescriptive models often describe quite well the performance of
humans that have been well trained for the task. This is particularly true
when prescriptive models include in their formulation, representations of
human limitations that constrain performance.
Top-Down Versus Bottom-Up
The top-down/bottom-up distinction refers to the extent that a model
is dictated by system goals or by human performance capabilities. A top-
down approach begins with a statement of system goals, then progressively
elaborates subgoals and functions until the modeler reaches a level at
which functions are accepted as primitives and are not explained further.
A bottom-up approach begins by defining a set of primitive elements at
both the human performance and the engineering levels. A system model
is then developed based on the Redefined set of primitive elements. Note
that this distinction refers to the evolution of the model, rather than to the
final model. Because of the nature of their evolution, top-down models
are lively to focus on output (system performance), whereas bottom-up
models are likely to focus on the processes leading to performance as well
as output
OCR for page 5
INTRODUCTION
Single-Task (Limited Scope) Versus Multitask (Comprehensive)
s
Most quantitative models have been developed with a single task
mind, although that task may involve several subtasks or processes. Single-
task models are models that range from simple movement to models for
manual control or signal detection that can involve perceptual, motor, and
even cognitive processes. ~~ respect to the concerns of this report such
single-msk models are viewed as being of limited scope. Multitask models,
on the other hand, are those that treat a variepr of such tasks within a
single uniting framework These models are referred to as comprehensive
HPMs.
MODELING METHODOLOGY
Another important way of characterizing HPMs is by the theories or
tools that underlie the model or serve as a basis for its development For
example, there are task network models (network and reliability models),
information processing models, control-theoretic models, and h~owledge-
based models. This is a particularly useful way of classifying comprehensive
or multitask models and is the basis for much of the discussion of modeling
approaches in Chapter 3.
One should not be confused by the many ways that HPMs can be
described or defined. In simplest terms, a model may be viewed as a "thing"
of which questions are asked about the real world. The ultimate role of a
model is to produce simulated performance (output or behavior) data. The
resulting data should be sufficiently similar to real performance data to be
useful to decision makers. Thus, a model is "good" if the same answers are
obtained from the model that would ultimately be obtained from the real
world, regardless of the particular modeling approach employed.
One final general point: A model of human performance implies the
existence of a model of the environment or system: in which that per-
formance takes place. Thus, in this report, human performance modeling
will almost always combine human with system performance models. The
manner in which the environment is modeled generally will dictate the
way in which the human is modeled and Vice versa. For example, discrete
event modeling of the system will tend to lead to task network models
for the operator, whereas continuous time system models would involve
corresponding representations of the humans.
1 "System, " in the report, refers to an interconnected set of parts making up a whole entity that
has a common purpose. Thus, one example of a human-machine system would consist of human,
turbine, reactor, etc., which collectively make up a nuclear power plant.
OCR for page 6
6
QU~NTI~TIYE MODELING OF HUMAN PERFOMklANCE
WHY USE HUMAN PERFORMANCE MODELS?
Processes That May Benefit from Their Use
Human performance models are used in two ways: (1) to develop
theories of human performance and (2) to design and evaluate systems.
These applications are not mutually exclusive. Lessons learned in theory
development can be of benefit to system design and vice versa.
Theory Development and Evaluation
~ develop a model, one must be specific about one's theories of
human performance. If a working model has been developed, the model
may be exercised to detains if the simulated behavior of the modeled
constituents corresponds to the behavior of those same constituents in the
real world under similar conditions. If the data obtained from the model
do not correspond to data obtained from the real world, it may be possible
to determine which aspects of the theory need to be reconsidered. If
the model is exercised under a variety of conditions and found to yield
satisfactory results, then confidence is gained for using the model to predict
the behavior of the constituents under novel conditions. Thus, the very
attempt at developing a model is highly useful in discovering where such
ambiguities exist.
System Design and Evaluation
Human performance models can play a role throughout the life pycle
of a system. They can be used in design to help establish system configura-
tion, parameter values, and operating procedures; in operation as integral
components of a system (against which actual human performance may
be compared); and in evaluation (e.g., of normal performance, accidents
and incidents, or specific missions). The greatest contn~ution. however. is
probably in design.
The importance of considering human performance during the design
process has become increasingly apparent in recent years. People are an
essential part of human-machine systems. It is substantially easier and less
expensive to consider how human capabilities will affect system operation
and modifsr the system before it is built, than modify it to conform to
human limitations after it has been constructed.
Generally, the first stages of system development involve specifying
functional requirements for the system and allocating those functions to
human or machine components. Later stages involve translating functional
a ,
OCR for page 7
INTRODUCTION
7
and performance requirements into design specifications; translating pro-
posed design specifications into a statement of projected performance of
each component, including people; and comparing projected performance.
The sequence, in general, consists of four stages:
1. Analyze the purpose of the system and identify the tasks that must
be accomplished to achieve it.
Describe the goals or performance requirements for the system.
Select a potential method for achieving those goals (i.e., a system
configuration at either a gross or a detailed level).
4. Model the configuration to obtain performance estimates and
compare the performance estimates to the stated goals. Then,
· if predicted performance does not satisfy the goals, redefine
the goals or rethink the method and try again or
· if the predictions and goals seem to match fairly well, simu-
late the configuration, test it with human subjects, and, based on the
results, proceed with development, make additional adjustments to
the goals, or modify the model as dictated by the experimental data.
This iterative procedure helps to extract those system characteristics
that are essential to meeting predefined system performance goals and are,
at the same the, responsive to human performance capacities and limita-
tions. It also provides a mechanism whereby HPMs can be systematically
improved.
Alternative (or Complementary) Methodologies to Modeling
Expert Opinion
A relatively straightforward and inexpensive approach to predicting
human perfo}~nance is to have experts predict what people will probably
do in a hypothetical system. Unfortunately, there is no way of knowing
in advance how valid these opinions will be. Moreover, the inherent
complexity and dynamic nature of the systems and problems of interest
make it extremely difficult for an expert, or group of experts, to account
for the effects of all possible interactions, particularly those with a low
probability of occurrence. Nevertheless, the analyses of an expert are
usually essential in defining initial alternatives and in evaluating the results
obtained by using other design methods.
OCR for page 8
8
Simulation
QUANTITATIVE MODELING OF HUMAN PERFORMANCE
Simulation refers to person-in-the-loop simulations that are, in fact,
person-machine models, except that the humanist and portions of the
environment are reaL Simulation has some unponant, although sometimes
overstated, advantages over most modeling techniques. There can be
little question about whether the people in the simulation are performing
like humans (they obviously are), but whether they are performing like
the humans of interest (e.g., fully trained operators of a system) can be
questioned. This will depend on the amount of training and practice given
to the operators of the simulation and the continuity of operation provided
in the experiments. If the purpose of the simulation is to provide data for
system design, the expense of building the simulator and the tune consumed
in designing it, trading the operators, and collecting data on them may
preclude drawing conclusions from their performance early enough to
properly influence design of the real system. Even when this is not the
case, the operating costs associated with person-in-the-loop simulation may
severely constrain the amount of data that can be collected, which will
adversely impact the scope of the system operation under investigation.
Despite these problems, simulation is, and will continue to be, an es-
sential element in complex system design because of its advantages relative
to testing in the real environment. Moreover, human performance mod-
eling will, for the foreseeable future, require experimental verification in
simulators (just as simulator results often require real-world verification).
Indeed, substantial synergy is possible between human performance mod-
eling and simulation. Models can be used to reduce the required amount
of simulation by determining critical areas of investigation, and they can be
used to understand and extrapolate Me results of simulation. Simulation
results can, in turn, be used to verify the model, identifier model parameters,
and generally advance model development.
Evaluation of Real Systems
\
Real-world testing and measurement represent the ultimate evaluation
of a design. However, the same objections can be raised for collecting data
by using real systems as for simulations: namely, that me data can come too
late for cost-effective design changes to be made. A more serious objection
concerns the potential risks of real-world operation if there is uncertainty
about the outcome.
2 Person-in-the-loop architecture refers to a system in which the human plays a more continu-
ously active role in its control and management.
OCR for page 9
INTRODUCTION
9
Laboratory E xperimentation
Basic laboratory experiments are also used to aid design decisions. In
particular, basic experiments (sometimes involving simple part-task simula-
tions) are often conducted to choose between design alternatives or to test
a particular concept or design. Care must be exercised in interpreting the
results of these experunents. For example, a laboratory experiment that
shows statistically significant differences may, or may not' reflect function-
ally significant differences in real-world performance. Moreover, because
the laboratory context is carefully controlled (i.e., eliminates or holds
constant many extraneous variables), the observed difference between al-
ternatives could disappear, or even be reversed, in the real-world setting
where these extraneous variables are a part of the task environment.
These comments are not meant to imply that laboratory experimenta-
tion is of no benefit but rather to suggest that its usefulness in predicting
real-world performance is variable. Laboratory experiments can be a rel-
atively inexpensive way to make early decisions when they must be made.
They also can be used to test or develop component models for single tasks
that are used in constructing more comprehensive models. In short, they
are useful adjuncts to, but not substitutes for, modeling, simulation, and
real-world evaluation.
Benefits of lIuman Performance Modeling
Each of the options discussed above may be appropnately applied to
the process of system design and development. However, in some cases
modeling offers advantages over other methods for obtaining the same, or
similar, data. Examples of the advantages of human performance modeling
are (1) its relative speed compared to other nonmodeling methods, (2)
its ability to give insight into whole new approaches or applications, and
(3) its cost effectiveness relative to dynamic simulation or real system
experimentation.
In other cases, human performance modeling can provide benefits not
obtainable by other methods. For example, a mode} can be used to provide
one or more of the following:
· a systematic framework around which to organize facts;
· an integrative tool which prompts consideration of aspects of a
problem that might, otherwise, have been overlooked; and
· a basis for extrapolating from the information given to draw new
hypotheses about human or system performance.
Broadly speaking, a model is nothing more than some modeler's rep-
resentation of some thing or process. It may not be necessary for a model
to be highly accurate to be useful (for example, a map of some area of
OCR for page 10
10
QUAN17TATI~E MODELING OF HUA~4N PERFORMANCE
the earth that is depicted on a two-dimensional plane surface uses the "flat
earth model," which is a misrepresentation, but the map is useful nonethe-
less). This suggests that the issue of model utility must be considered in
addition to in validity, as long as its users recognize that a useful model is
not necessarily completed valid in terms of process as well as output. As
discussed earlier, a model may accurately predict the output; however, the
process used to arrive at this prediction may not accurately reflect the way
in which a human would arrive at the same outcome.
Genealogy of Human Performance Models
The history of HPMs dates back to World War II. Of interest are the
antecedents, and possible components, of the approaches to modeling de-
scribed in this report Figure 1-1 summarizes this history diagrammatically
by highlighting four main approaches to human performance modeling:
information processing approaches, control theory approaches, task net-
work approaches (network and reliability modeling), and knowledge-based
approaches. Each of these developments is considered in turn.
Information-Processing Models
The Mathematical Theo f Information (Shannon and Weaver,
1949), together with the ideas of Wiener (1950) concerning feedback con-
trolled systems that he called cybernetics were the precursors of a whole
new way to think about human behavior. Because it then became possible
to think concretely about the abstract concept "information," and because
information input, processing, and output represented human activities as
well as activities that could be ascn~ed to a machine, it was only natural
for the information-processing analogy to be extended to the analysis of
human performance.
This new approach was torpified by Broadbent (1958) who formulated a
block diagram analysis of information flow in human perception and mem-
ory. Although Broadbent's ideas were qualitative, they laid the foundations
for quantitative models of elementary human information processing op-
erations. As Neisser (1967) pointed out, this approach is not a computer
analogy in the sense that the brain behaves like a computer, but rather a
programming analogy that gave rise to a Table research strategy founded
on the idea of d~scove~g the algorithms by which human information
processing takes place.
This approach spawned models of visual search and identification,
short- and long-term memory, reaction time underlying simple decision
processes, and movement control, to mention just a few. It has led to
numerous attempts to formulate block diagrams of human information
OCR for page 11
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OCR for page 12
12
QUANTITATIVE MODELING OF HUMAW PERFORMANCE
processing. From the viewpoint of this report, however, the models were of
isolated psychological functions rather than integrative human performance.
The Human Operator Simulator (HOS), discussed in Chapter 3, was
one of the first attempts to capture component information-processing
concepts in the form of an aggregated model that might be applied to
system design and evaluation (Lane, Strieb, and Leyland, 1979~.
Control Theory Models
Interest in manual control models was first stimulated by the need
to understand how humans control antiaircraft guns and other closed-
loop systems. The seminal paper on this subject was by Justin (1947),
a British electrical engineer who fit first- and second-order differential
equations to the experimentally observed transient response of the human
operator to step-input signals. This was an insightful analysis based on the
understanding of servomechanisms at the time.
During this period a number of experimental studies systematically
examined the effects of system variables on human tracHng performance
(Helson, 1944; Wilson and Hill, 1948; Rockway, 1955~. At about this
time Birmingham and Taylor (1954) published their landmark paper on
"Man-Machine Control Systems." The concepts of quickening and aiding
were introduced, and the theory was put forth that man operated most
effectively when system constraints permitted performance analogous to
that of a simple amplifier.
In 1956, ELkind provided the first comprehensive, systematic data and
models of human control as a function of a variety of continuous band-
limited Gaussian input signals and different controlled element dynamics.
Elkind pioneered in the empirical measurement and analysis of power
density and cross-power densitr spectra, as well as in the technology for
measuring human tracking performance. Although the technology for such
measuring has made giant strides since the 1950s, ELkind's data and analysis
have never been seriously challenged.
Meanwhile, in the early 1950s, McRuer began advocating that analysis
of the human pilot could be done in the same terms as analysis of the
balance of the aircraft flight control system. He teamed with Krendel to
generate new data and to undertake the first comprehensive review and
analysis of all the manual control data available at the time. Their report,
"Dynamic Response of Human Operators" (McRuer and Krendel, 1957)
was the bible for work in this field for at least 10 years. McRuer and Krendel
codified and systematized data in the forte of quasilinear describing function
models, together with rules for their adaptation, as a function of the varied
of system variables known at the time. A spin-off of their analysis was the
Crossover Model, a simplified conception based on the observation that
OCR for page 13
INTRODUCTION
13
when the human and the system were represented as a unit, a simpler
form of the model resulted (McRuer, Graham, Krendel, and Reisener,
1965~. In effect, the human adapted his behavior so that the combination
behaved like a simple first-order system with limited bandwidth. It was also
found that systems that approximated a simple integration and, therefore,
allowed the operator to behave like a proportional controller (i.e., a gain
or amplification factor) were preferred. This confirmed Birmingham and
ylor's "simple amplifier" tenet.
In the 1960s, modern control theory, using a state variable approach
and optimization techniques that permitted closed-form solutions to com-
plex control problems, was applied to the manual control problem. Baron
and Kleinman (1969) proposed a model for the operator, based on optimal
control and estimation theory, to account for both control itself and the
information processing necessary to support it. This model was developed
further, with contributions from Levison, and has come to be known as
the Optimal Control Model (OCM). The OCM introduced the concepts of
observation and motor noise as stochastic components of the operator that
limited human performance. It also made explicit the need for an internal
model of system inputs and dynamics as a prerequisite for successful track-
ing performance. These concepts have been used for the quantification
of attentional workload in the context of manual control (Lesson, Unkind,
and Ward, 1971) and for exploring the question of what is learned as one
acquires tracking skill (Levison, 1979~. The OCM has been applied widely,
and the ~nformation-processing portion of the model has been extended to
tasks other than manual control
lithe introduction of automation in aircraft cockpits and the vast in-
crease in complete of the avionics resulting from it have forced consid-
erat~on of manual aircraft control in the larger context of aircraft systems
management. These developments have led to the generalization of models
to include the operation of management functions. The Procedure Oriented
Crew (PROCRU) model (Baron, Zacharias, Muralidharan, and Lancraft,
1980) was a response to this need. PROCRU, a computer simulation
model, is a derivative of the OCM that incorporates the execution of pro-
cedures in the context of manual control. It introduces the concept of
expected net gain, a generalization of the performance index, as a means
of predicting priorities among procedures to be executed.
Task Network Models
In parallel with these advances, the operations research community
developed sophisticated models of system processes using a task network
approach. With this approach a complex system is represented by a network
OCR for page 14
14
QUANTITATIVE MODELING OF HUMAN PERFORMANCE
of component processes, each modeled by statistical distributions of com-
pletion time and probability of success. The resultant computer program is
run as a Monte Carlo simulation to predict the statistical distributions of
measures of overall system performance. The PERT methodology for man-
agement of system development was one outgrown of this approach. Siegel
and Wolf (1969) first applied task network modeling to predict human per-
formance in a systems context. One innovative concept they introduced
was that of a moderator function. Human capacities were postulated to be
sensitive to certain global variables such as motivation or stress. ~ explore
the impact of these variables, moderator functions shifted the time dism~u-
tions or completion probabilities for all component tasks to be performed
by the human operator based on the setting of the moderator [unction.
This permitted sensitivity analyses to be run easily to test the robustness of
performance in the face of variations in stress level or motivation.
At about the same time, Swain and his colleagues working at Sandia
became concerned with human reliability in the Navy and, later, in the nu-
clear power industry. They collected data on the probability of successfully
completing some elemental human operations such as closing valves, read-
ing displays, or carrying out simple procedures. System reliability analysis,
which predicts the performance of mechanical components in a systems
context, proceeds according to methods not unlike network analysis. Swain
(1963; Swain and Guttmann, 1980) developed methods for incorporat-
ing elements of the network, reflecting the reliability of both human and
mechanical components of a system, in order to improve overall system
reliability estimates.
The task network approach was further stimulated by the development
of Systems Analysis of Integrated Networks of Tasks (SAINT), a simulation
language specifically designed to make it easy to build task network models
of human and system performance (Pritsker, Wortman, Seum, Chubb, and
Seifert, 1974~. This language has been used to study performance in a wide
range of systems including digital avionics systems, command and control
networks, and a hot strip mill; SLAM II represents the current state of the
art with respect to task network simulation languages and modeling tools.
Knowledge-Based Models
About the same time that component models of information pro-
cessing were being developed, Newell, Shaw, and Simon (1958) and their
colleagues began work on the development of computer programs capa-
ble of logical reasoning. This work was based on the realization that a
computer is basically a device for manipulating symbols, and that solving
numerical problems (the purpose for which computers were developed)
is only one example of symbol manipulation The work of Newell et al.
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INTRODUCTION
15
led to the development of the General Problem Solver program (GPS;
Newell and Smon, 1972), which was capable of m~mic~ng many of the
behaviors observed when people attempt to solve logical problems with the
general complexity of those in Scientific American puzzle articles. Newell,
Simon, and their many colleagues and followers have pushed this work
on knowledge-based models forward very rapidly, and today, many of the
logical and programming techniques that they developed are the heart of
modern artificial intelligence and expert system programs. In addition, the
concepts they developed for tong about thought are central to today's
study of cognitive psychology.
Most of the work in this field has centered on modeling human problem
solving rather than human-machine systems. More recently, though, sev-
eral experimental studies of limited human-machine operations have been
conducted. Many people believe that human-machine system modeling is
the wave of the future, especially for situations in which the modeling effort
views a person as a planner rather than a sensor or movement controller.
1
Representative terms from entire chapter:
task network
