  In this paper we explore the Linear-Quadratic Regulator (LQR) to model
movement of the mouse pointer. We propose a model in which users are assumed to
behave optimally with respect to a certain cost function. Users try to minimize
the distance of the mouse pointer to the target smoothly and with minimal
effort, by simultaneously minimizing the jerk of the movement. We identify
parameters of our model from a dataset of reciprocal pointing with the mouse.
We compare our model to the classical minimum-jerk and second-order lag models
on data from 12 users with a total of 7702 movements. Our results show that our
approach explains the data significantly better than either of these previous
models.
