The ability to transfer sensorimotor skill components to new actions and the capacity to use skill components from whole actions are characteristic of the adaptability of the human sensorimotor system. However, behavioral evidence suggests complex limitations for transfer after combined or modular learning of motor adaptations. Also, to date, only behavioral analysis of the consequences of the modular learning has been reported, with little understanding of the sensorimotor mechanisms of control and the interaction between cortical areas. We programmed a video game with distorted kinematic and dynamic features to test the ability to combine sensorimotor skill components learned modularly (composition) and the capacity to use separate sensorimotor skill components learned in combination (decomposition). We examined motor performance, eye-hand coordination, and EEG connectivity. When tested for integrated learning, we found that combined practice initially performed better than separated practice, but differences disappeared after integrated practice. Separate learning promotes fewer anticipatory control mechanisms (depending more on feedback control), evidenced in a lower gaze leading behavior and in higher connectivity between visual and premotor domains, in comparison with the combined practice. The sensorimotor system can acquire motor modules in a separated or integrated manner. However, the system appears to require integrated practice to coordinate the adaptations with the skill learning and the networks involved in the integrated behavior. This integration seems to be related to the acquisition of anticipatory mechanism of control and with the decrement of feedback control.
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http://dx.doi.org/10.1002/hbm.24215 | DOI Listing |
J Adv Nurs
January 2025
Professor of Person Centred Healthcare, South Western Sydney Nursing & Midwifery Research Alliance, The Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.
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Design: Through an explanatory sequential mixed-methods evaluation study, we assessed the programme's effectiveness in building capacity, sustaining practice changes and fostering a culture of continuous improvement.
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Neural Netw
January 2025
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China. Electronic address:
In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and neighborhood structures. This results in a distribution shift problem for entity features in the embedding space during graph representation learning. Most of existing inductive knowledge graph embedding methods focus mainly on the representation learning of new entities, neglecting the negative impact caused by distribution shift of entity features.
View Article and Find Full Text PDFTop Stroke Rehabil
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View Article and Find Full Text PDFBehav Anal Pract
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Department of Psychology, Georgia Southern University, Savannah, GA USA.
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