Rethinking probabilistic sensorimotor sequence learning: Focus on probabilistic systems instead of simple patterns

Many complex motor skills follow probabilistic rules that determine which movement transitions are efficient, stylistically appropriate, and contextually purposeful. However, research on sensorimotor sequence learning has largely focused on deterministic sequences or basic probabilistic regularities, overlooking the acquisition of broader rule-based sequence structures. In contrast, fields such as psycholinguistics emphasize learning entire probabilistic rule systems. This difference in focus limits comparability of results from sequence learning tasks across scientific disciplines and thus interpretations about domain-general and domain-specific mechanisms of probabilistic sequence learning. This article highlights how motor control research can expand its focus to the learning of global probabilistic rulesets by integrating sequence generation algorithms and post-acquisition generalization tests from psycholinguistics. First, we compare sequence construction and learning assessment strategies in both fields, demonstrating how algorithms from artificial grammar experiments and generalization assessments can be adapted to motor sequence learning. Second, we propose a practical framework for experimental designs in motor control that distinguishes between local statistical features and global probabilistic systems. We outline key methodological considerations for sequence construction, acquisition, and retrieval testing and want to foster more systematic and comprehensive investigations into probabilistic sensorimotor sequence learning.