Dynamic population coding of kinematic structure across executed and observed actions in primate premotor cortex

Neurons active during both action execution and observation [mirror neurons (MirNs)] are central to theories of action understanding, yet what they represent remains debated: abstract goals, static grips, or movement kinematics? We recorded 433 neurons from macaque premotor cortex during execution and observation of reach-to-grasp actions. Population analyses revealed grasp-specific information in both conditions, broadly distributed across neurons and dynamically reconfigured over time. Generalization across task phases was limited, indicating time-specific and evolving population codes rather than static representations. Execution and observation were linked by a shared, partially overlapping population geometry that supported reliable cross-condition classification, with the strongest alignment emerging during movement and hold. Neural activity was systematically related to multidimensional hand kinematics, and these relationships generalized across neuronal populations and across agents. Together, these findings support a dynamic population-level account of MirN function in which premotor circuits integrate visual and motor signals to represent and anticipate the unfolding structure of others’ actions.