Statement of Interest

Lessons from modeling perceptual, cognitive and motor learning\
in a dynamic computer-based task

Bonnie E. John

Human-Computer Interaction Institute

and the Departments of Psychology and Computer Science

Carnegie Mellon University

Pittsburgh PA 15213

bej@cs.cmu.edu

To be effective in simulating human behavior, a computational cognitive
model must behave like a human, not simply get the job done with the
least effort or in the least time. In particular, a model of human
behavior in a highly interactive task will have to show human-scale
performance improvement (or *learning*) on the same number of trials of
a task in which the human shows this improvement. Our model of the
KA-ATC© task (Ackerman & Kanfer, 1994) used the Soar computational
cognitive architecture (Newell, 1990), in combination with EPIC
peripherals (Meyer & Kieras, 1997), to demonstrate human-like learning
in the first two trials of the task. The most important lesson from this
model is that it learns several different types of knowledge to produce
the rapid speed up between the first and second trials observed in human
data. It learns to visually search more efficiently; it learns to order
subtasks; it learns which keys to hit. Furthermore the relative
importance of each of these types of knowledge will depend heavily on
the task being learned, but, for fast-paced tasks of the complexity of
the KA-ATC© task, all three contribute substantially. We conclude from
these results that researchers building simulated humans will need to
take learning seriously in areas beyond central cognition.

In addition to the lessons for the *content* of human models, we also
learned a substantial amount about the *process* of constructing such
models. Just as no human performs the KA-ATC© task without any prior
knowledge or skills, our model had to include capabilities, knowledge,
and skills learned from prior experience with related tasks. To achieve
this aspect of human behavior, we "integrated" along several dimensions.
As discussed above, we integrated learning in perceptual, cognitive and
motor aspects of the task. We used two established architectures:
central cognition from Soar (Newell, 1990), and the perceptual and motor
peripherals from EPIC (Meyer & Kieras, 1997). We drew inspiration from a
general mechanism for memorization widely used in the Soar community
(called *data chunking*, Newell, 1990). We re-used a specific mechanism
for learning from instruction from a model of learning to use an
automatic teller machine (Vera, Lewis & Lerch, 1993). In addition, we
re-used actual code from a dual-task studied by Martin-Emerson and
Wickens (1992) and modeled in both EPIC (Kieras & Meyer, 1995) and
EPIC-Soar (Chong & Laird,1997; Lallement & John, 1998). In this last, we
re-used both the hand-written code for the dual-task model and the
knowledge learned by that model as it performed its task. That learned
knowledge (how and when to switch attention) transferred to the KA-ATC
task.

At the 2000 AAAI Fall Symposium on Simulating Human Agents, we would
like to discuss the need for all types of integration and our
experiences in our small steps towards that goal.

# References

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program*. Technical report, University of Minnesota, Department of
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Chong, R. & Laird, J. (1997). Identifying dual-task executive process
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Kieras, D. E. & Meyer, D. E. (1995) *An overview of the EPIC
architecture for cognition and performance with application to
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Lallement, Y. & John, B. E. (1998). Cognitive architecture and modeling
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Newell, A. (1990). *Unified theories of cognition*. Harvard University
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