In the current understanding of motor theory, successful movements are based on a good sensory estimate of the initial state as well as a good prediction of the change in state, resulting from the output of motor control signals (e.g., Wolpert et al., 1995; transferred to sports practice: Hossner et al., 2020). In this context, foundational studies have examined movement adaptations in the presence of force-fields. The findings of such studies reveal that when stringing together multiple state estimates – i.e., behavioral sequencing – the quality of control at a link in the behavioral chain depends on both the preceding (lead-in) and following (follow-through) links in the chain (Howard et al., 2015). We address this finding in the learning of more complex movement tasks. In the Ξ-task (Hossner, 2004), we investigate the transitions between states in the ambidextrous positioning of two 3D levers (Hossner & Ehrlenspiel, 2010; see video), whereas in the s-vis-mot task, we examine the movements of a 2D cursor to its target wherein the cursor is controlled multidimensionally by the force-moment inputs of both index and ring fingers (Hofer et al., 2017).
