Motor sequence learning involves better prediction of the next action and optimization of movement trajectories

Learning new sequential movements is a fundamental skill for many animals. Although the behavioral manifestations of sequence learning are clear, the underlying mechanisms remain poorly understood. Motor sequence learning may arise from three distinct processes: (1) improved execution of individual movements independent of their sequential context; (2) enhanced anticipation of “what” movement should be executed next, enabling faster initiation; and (3) the development of motoric sequence-specific representations that encode “how” movements should be optimally performed within a sequence. However, many existing paradigms conflate the "what" and "how" components of learning, as participants often acquire both the sequence content (what to do) and its execution (how to do it). This overlap obscures the distinct contributions of each mechanism to motor sequence learning. In this study, we disentangled these mechanisms using a continuous reaching task. Performance in trained sequences was compared to random sequences to rule out improvements attributable solely to isolated movement execution. By also varying how many upcoming targets were visible we assessed the role of anticipation in learning. When participants could only see one future target, improvements were mostly due to them learning which target would come next. When they could see four future targets, participants immediately demonstrated fast movement times and increased movement smoothness, surpassing late-stage performance in the one target condition. Crucially, even with full visibility of future targets, participants showed further sequence-specific learning caused by a continuous optimization of movement trajectories. Follow-up experiments revealed that the learned sequence representations were effector-specific and encoded contextual information of four movements or longer. Our paradigm enables a clear dissociation between the "what" and "how" components of motor sequence learning and provides compelling evidence for the development of effector-specific sequence representations that guide optimal movement execution.