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Article summary:
1. A model of motor learning was proposed to identify a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements.
2. The model was applied to force field adaptation tasks where normal reaching movements are disturbed by an external force imposed on the hand, and was able to adapt to the force field without prior knowledge about the arm or environment.
3. The model does not require desired trajectories, yields movements without specifying movement duration, and predicts force generation patterns by exploring the environment.
Article analysis:
The article is generally reliable and trustworthy in its presentation of the research findings. It provides a clear explanation of the research methodology used, as well as detailed descriptions of the results obtained from applying this methodology. The authors also provide evidence for their claims in terms of kinematic features observed before and after force field adaptation, as well as shifts in end-point equilibrium position and static force modulation that were induced by their model's structure and learning algorithm. Furthermore, they discuss potential implications for how the central nervous system may control point-to-point reaching movements without specifying a desired trajectory.
The article does not appear to be biased or one-sided in its reporting; it presents both sides equally by discussing existing models that assume desired trajectories as well as those based on optimal feedback control theory which do not require such assumptions. Additionally, there is no promotional content present in the article; instead it focuses solely on presenting research findings objectively.
The only potential issue with this article is that it does not explore any counterarguments or alternative explanations for its findings; however, given that this is an initial study into this area of research, such exploration may be better suited for future studies once more data has been collected and analyzed.