Abstract
Activities of daily living rely on our ability to acquire new motor skills composed of precise action sequences. Early learning of a new sequential skill is characterized by steep performance improvements that develop predominantly during rest intervals interspersed with practice, a form of rapid consolidation. Here, we ask if the millisecond level neural representation of an action performed at different locations within a skill sequence contextually differentiates or remains stable as learning evolves. Optimization of machine learning decoders to classify sequence-embedded finger movements from MEG activity reached approximately 94% accuracy. The representation manifolds of the same action performed in different sequence contexts progressively differentiated during rest periods of early learning, predicting skill gains. We conclude that sequence action representations contextually differentiate during early skill learning, an issue relevant to brain-computer interface applications in neurorehabilitation.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Declaration of interests. The authors declare no competing interests.
New Results, new control analyses, more discussion, and more findings.
