Modeling Motor Control in Continuous Time Active Inference: A Survey

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.