ABSTRACT
We have developed a process model that learns in multiple ways using the Soar
chunking mechanism while finding faults in a simple control panel device. The
model accounts very well for measures such as problem solving strategy, the
relative difficulty of faults, average fault-finding time, and, because the model
learns as well, the speed up due to learning when examined across subjects,
faults, and even series of trials for individuals. However, subjects tended to take
longer than predicted to find a fault the second time they completed a task. To
examine this effect, we compared the model’s sequential predictions-the order
and relative speed that it examined interface objects-with a subject’s
performance. We found that (a) the model’s operators and subject’s actions were
applied in basically the same order; (b) during the initial learning phase there
was greater variation in the time taken to apply operators than the model
predicted; (c) the subject appeared to spend time checking their work after
completing the task (which the model did not). The failure to match times on
the second time seeing a fault may be accounted for by the subject spent
checking their work whilst they learn to solve the fault-finding problems. The
sequential analysis reminds us that though aggregate measures can be well
matched by a model, the underlying processes that generate these predictions
can differ.
