  Optimal feedback control (OFC) is a theory from the motor control literature
that explains how humans move their body to achieve a certain goal, e.g.,
pointing with the finger. OFC is based on the assumption that humans aim to
control their body optimally, within the constraints imposed by body,
environment, and task. In this paper, we explain how this theory can be applied
to understanding Human-Computer Interaction (HCI) in the case of pointing. We
propose that the human body and computer dynamics can be interpreted as a
single dynamical system. The system state is controlled by the user via muscle
control signals, and estimated from observations. Between-trial variability
arises from signal-dependent control noise and observation noise. We compare
four different models from optimal control theory and evaluate to what degree
these models can replicate movements in the case of mouse pointing. We
introduce a procedure to identify parameters that best explain observed user
behavior. To support HCI researchers in simulating, analyzing, and optimizing
interaction movements, we provide the Python toolbox OFC4HCI. We conclude that
OFC presents a powerful framework for HCI to understand and simulate motion of
the human body and of the interface on a moment by moment basis.
