A priori prediction of skilled human performance has the
potential to be of great practical value but is difficult
to carry out. This paper reports on an approach that
facilitates modeling of human behavior at the level of
cognitive, perceptual, and motor operations, following the
CPM-GOMS method (John, 1990). CPM-GOMS is a powerful
modeling method that has remained underused because of the
expertise and labor required. We describe a process for
automatically generating CPM-GOMS models from a
hierarchical task decomposition expressed in a
computational modeling tool, taking advantage of reusable
behavior templates and their efficacy for generating
zero-parameter a priori predictions of complex human
behavior. To demonstrate the process, we present a model
of automated teller machine interaction. The model shows
that it is possible to string together existing behavioral
templates that compose basic HCI tasks, (e.g., mousing to
a button and clicking on it) in order to generate powerful
human performance predictions. Because interleaving of
templates is now automated, it becomes possible to
construct arbitrarily long sequences of behavior. In
addition, the manipulation and adaptation of complete
models has the potential of becoming dramatically easier.
Thus, the tool described here provides an engine for
CPM-GOMS that may facilitate computational modeling human
performance at the millisecond level.
![[first author]](images/one_arial_12.jpg){kind=small}
