  We present Intermittent Control (IC) models as a candidate framework for
modelling human input movements in Human--Computer Interaction (HCI). IC
differs from continuous control in that users are not assumed to use feedback
to adjust their movements continuously, but only when the difference between
the observed pointer position and predicted pointer positions become large. We
use a parameter optimisation approach to identify the parameters of an
intermittent controller from experimental data, where users performed
one-dimensional mouse movements in a reciprocal pointing task. Compared to
previous published work with continuous control models, based on the
Kullback-Leibler divergence from the experimental observations, IC is better
able to generatively reproduce the distinctive dynamical features and
variability of the pointing task across participants and over repeated tasks.
IC is compatible with current physiological and psychological theory and
provides insight into the source of variability in HCI tasks.
