Abstract
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.
Highlights
Computational modelling has driven our understanding of human sensorimotor control by supplementing experimental results and motivating new hypotheses [1,2,3,4]
Here we propose a new, mixed-horizon approach in modelling movement planning and execution where the planning stage is represented by an infinite-horizon optimal feedback controller, and the execution stage is represented by a finite-horizon OFC, with the infinite-horizon controller providing movement durations to the finite-horizon controller
Our sensitivity analysis demonstrated that our models can produce kinematics and dynamics with some systematic differences that depend on the parameters and cost functions
Summary
Computational modelling has driven our understanding of human sensorimotor control by supplementing experimental results and motivating new hypotheses [1,2,3,4]. Optimality principles have been proposed to explain human movement and solve issues of redundancy through trade-offs between different elements of the cost function, for example task goals and energy consumption [11,12,13,14]. Overall these computational approaches have been very successful at reproducing and explaining human-like behaviours [15,16,17,18]. There are subtle differences in implementations of different optimal control paradigms that could result in meaningful behavioural differences, so the motivation of using any one specific algorithm is not always clear
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