Humans’ rapid adaptation to new dynamics is a crucial ability for interacting with the world. By combining the controlled manipulations of sensorimotor adaptation studies with the long-term learning approach of complex tasks, this study shows that the learned motor memory is continually tuned, and that retention increases with long-term training. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription Receive 12 digital issues and online access to articles Prices may be subject to local taxes which are calculated during checkout Franklin, D.
Computational mechanisms of sensorimotor control. Neuron 72, 425–442 (2011). This review outlines the main computational mechanisms behind sensorimotor control.
Oh, Y. & Schweighofer, N. Minimizing precision-weighted sensory prediction errors via memory formation and switching in motor adaptation. Neurosci. 39, 9237–9250 (2019).
This paper explains whether we tune an existing motor memory or form a new one, and proposes that spatial generalization would decrease while an internal model is learned. Article CAS PubMed PubMed Central Google Scholar Leib, R. & Franklin, D. Error prediction determines the coordinate system used for the representation of novel dynamics. eLife 14, e84349 (2025).
This paper develops a model that proposes that we represent our motor memories in weighted combinations of coordinate systems depending on their reliability. Park, S.-W., Dijkstra, T. Learning to never forget—time scales and specificity of long-term memory of a motor skill.
Neurosci. 7, 111 https://doi.org/10.3389/fncom.2013.00111 (2013). This paper demonstrates the formation over two months, and long-term retention over years, of a bimanual polyrhythmic motor skill. Article PubMed PubMed Central Google Scholar Dimitriou, M.
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