Deep kinematic inference affords efficient and scalable control of bodily movements

The functional role of conscious sensation of movement

This paper proposes a new framework for investigating neural signals sufficient for a conscious sensation of movement and their role in motor control. We focus on signals sufficient for proprioceptive awareness, particularly from muscle spindle activation and from primary motor cortex (M1). Our review of muscle vibration studies reveals that afferent signals alone can induce conscious sensations of movement. Similarly, studies employing peripheral nerve blocks suggest that efferent signals from M1 are sufficient for sensations of movement. On this basis, we show that competing theories of motor control assign different roles to sensation of movement. According to motor command theories, sensation of movement corresponds to an estimation of the current state based on afferent signals, efferent signals, and predictions. In contrast, within active inference architectures, sensations correspond to proprioceptive predictions driven by efferent signals from M1. The focus on sensation of movement provides a way to critically compare and evaluate the two theories. Our analysis offers new insights into the functional roles of movement sensations in motor control and consciousness.

Active inference as a theory of sentient behavior

This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence.