The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Temporal structure of motor variability is dynamically regulated and predicts motor learning ability
Open accessAbstract
Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.
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Cites
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Cited by
Humans use directed and random exploration to solve the explore–exploit dilemma.
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0038199 · Cited by 580
Humans can continuously optimize energetic cost during walking
Selinger, J. C., O’Connor, S. M., Wong, J. D., & Donelan, J. M. (2015) · Current Biology · doi:10.1016/j.cub.2015.08.016 · Cited by 395
Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review
Saha, S., & Baumert, M. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00087 · Cited by 325
Hippocampal CA2 activity patterns change over time to a larger extent than between spatial contexts
Mankin, E. A., Diehl, G. W., Sparks, F. T., Leutgeb, S., & Leutgeb, J. K. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.001 · Cited by 279
Wilson, P. H., Smits‐Engelsman, B., Caeyenberghs, K., Steenbergen, B., Sugden, D., Clark, J., Mumford, N., & Blank, R. (2017) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.13530 · Cited by 308
Metadata
- Authors
- Wu, H. G., Miyamoto, Y. R., Castro, L. N. G., Ölveczky, B. P., & Smith, M. A.
- Venue
- Nature Neuroscience
- Published
- 2014-01-12
- Volume
- 17
- Issue
- 2
- Pages
- 312-321
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2021742495
- Cited by
- 675
- References
- 13
- FWCI
- 26.05
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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