Intermittent movement control emerges from information-based planning

Dominant models of reactive motion control in humans, based on optimal feedback control, predict smooth trajectories that reflect averaged behaviors. However, the observation of individual movements suggests that mammalian motor control is inherently discrete, with movement corrections occurring at a rate that depends on task demands and sensory information quality. To address these limitations, we introduce the Information Predictive Control (IPC) framework that integrates model predictive control with information theory, which triggers movement corrections only when unexpected deviations occur and corrections are likely to succeed. By quantifying “surprise” relative to anticipated internal and external states, IPC produces successful movements while robustly responding to sensorimotor noise, task constraints, and target variability. Simulations demonstrate IPC’s ability to reproduce human-like responses to novel force fields during reaching, continuous target tracking, and adaptive planning under noise, while dynamically adjusting the planning horizon in complex, unpredictable environments.