  Computational optimal feedback control (OFC) models in the sensorimotor
control literature span a vast range of different implementations. Among the
popular algorithms, finite-horizon, receding-horizon or infinite-horizon
linear-quadratic regulators (LQR) have been broadly used to model human
reaching movements. While these different implementations have their unique
merits, all three have limitations in simulating the temporal evolution of
visuomotor feedback responses. Here we propose a novel approach - a
mixed-horizon OFC - by combining the strengths of the traditional
finite-horizon and the infinite-horizon controllers to address their individual
limitations. Specifically, we use the infinite-horizon OFC to generate
durations of the movements, which are then fed into the finite-horizon
controller to generate control gains. We then demonstrate the stability of our
model by performing extensive sensitivity analysis of both re-optimisation and
different cost functions. Finally, we use our model to provide a fresh look to
previously published studies by reinforcing the previous results, providing
alternative explanations to previous studies, or generating new predictive
results for prior experiments.
