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Motor learning is a central focus of several disciplines including kinesiology, neuroscience and rehabilitation. However, given the different traditions of these fields, this interdisciplinarity can be a challenge when trying to interpret evidence and claims from motor learning experiments. To address this issue, we offer a set of ten guidelines for designing motor learning experiments starting from task selection to data analysis, primarily from the viewpoint of running lab-based experiments. The guidelines are not intended to serve as rigid rules, but instead to raise awareness about key issues in motor learning. We believe that addressing these issues can increase the robustness of work in the field and its relevance to the real-world. 

Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning. The presence of large degrees of freedom (DoFs) available in these systems makes it difficult to balance the trade-off between learning the full dexterity and accomplishing manipulation goals. The motor learning literature argues that humans use motor synergies to reduce the dimension of control space. Using the low-dimensional space spanned by these synergies, we develop a computational model based on the internal model theory of motor control. We analyze the proposed model in terms of its convergence properties and fit it to the data collected from human experiments. We compare the performance of the fitted model to the experimental data and show that it captures human motor learning behavior well. 

Background: Despite tremendous advances in the treatment and management of stroke, restoring motor and functional outcomes after stroke continues to be a major clinical challenge. Given the wide range of approaches used in motor rehabilitation, several commentaries have highlighted the lack of a clear scientific basis for different interventions as one critical factor that has led to suboptimal study outcomes. Objective: To understand the content of current therapeutic interventions in terms of their active ingredients. Methods: We conducted an analysis of randomized controlled trials in stroke rehabilitation over a 2-year period from 2019-2020. Results: There were three primary findings: (i) consistent with prior reports, most studies did not provide an explicit rationale for why the treatment would be expected to work, (ii) most therapeutic interventions mentioned multiple active ingredients and there was not a close correspondence between the active ingredients mentioned versus the active ingredients measured in the study, and (iii) multimodal approaches that involved more than one therapeutic approach tended to be combined in an ad-hoc fashion, indicating the lack of a targeted approach. Conclusion: These results highlight the need for strengthening cross-disciplinary connections between basic science and clinical studies, and the need for structured development and testing of therapeutic approaches to find more effective treatment interventions.