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The
goal of my research is to understand how people organize knowledge that
they
acquire from their diverse experiences to produce intelligent behavior.
The
concern is very much with how it is all put together and this has led to
the
focus
on what are called "unified theories of cognition." A unified theory is
a
cognitive
architecture that can perform in detail a full range of cognitive tasks
Our
theory is called ACT-R (Anderson
& Lebiere, 1998) and takes the form of a
computer
simulation which is capable of performing and learning from the same
tasks
that subjects in our laboratories work at.
ACT-R
is also an instance of a hybrid cognitive architecture in that it represents
knowledge
symbolically as rules and facts but also has a neurally-based activation
process
that determines which facts and rules get deployed in which situations.
We
have engaged in extensive analyses of the situations which people have
to deal
with
in order to understand how each of these components should work together
to
yield
adaptive behavior.
Our
research has two major branches. First, in the laboratory we are looking
at how
people
learn and solve problems in very well-defined situations. Here we are interested
in
things like how strategies for problem-solving evolve, how people discover
things about
a
new domain, how they deal with the working memory load imposed by the tasks,
and
how
they get faster at accessing information relevant to task performance.
Our subjects
all
interact with experiment-running computer programs and we try to develop
ACT-R
simulations
that can interact with the same programs, take the same actions, make the
same
eye movements, and display the same latencies. The emphasis in this research
is
very much in getting the detail of the simulation to match up with the
detail of the
behavior.
The
other branch of our research involves a much broader focus. We have taken
on
modeling
the cognitive competences that are taught in the domains of mathematics,
computer
programming, and cognitive psychology. Much of the motivation for this
research
is to be able to tap into real situations where people learn and solve
problems
and
understand the implications of these domains for the cognitive architecture.
We
have built larger-grain ACT-R simulations that are capable of solving problems
in
these domains and have developed computer-based instruction around these
cognitive
models. Many of these computer-based instructional systems have the
cognitive
models as a component and attempt to understand student behavior by
actually
simulating what the student is doing in real time. These are called cognitive
tutors
and are currently being used to help teach courses in schools around the
country.
Much
of this research has gone beyond the original goals of understanding human
cognition
and now is part of a major effort to produce a significant improvement
in
American
mathematics education.
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