Modeling Motor Control in Continuous Time Active Inference: A Survey

Abstract

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on optimal control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations. Active inference offers a modern formulation of these ideas, in terms of inferential mechanisms and prediction error-based control, which can be linked to neural mechanisms of living organisms. This article provides a technical illustration of active inference models in continuous time and a brief survey of active inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control. Crucially, in active inference, all these different facets of motor control emerge from the same optimization process - namely, the minimization of free energy - and do not require designing separate cost functions. Therefore, active inference provides a unitary perspective on various aspects of motor control that can inform both the study of biological control mechanisms and the design of artificial and robotic systems.