An explicit strategy facilitates adaptation to virtual surgeries by biasing the recruitment of existing synergies.

Overview

1. Laboratory of Physiology, Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy
2. Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
3. IRCCS Centro Neurolesi Bonino-Pulejo, 98123 Messina, Italy
4. Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita City, Osaka 565-0871, Japan
5. Department Economics and Finance, University of Rome Tor Vergata, 00133 Rome, Italy
6. Clinical Neurophysiology Research Unit, Oasi Research Institute-IRCCS, 94018 Troina, Italy
7. Department of Systems Medicine, Centre of Space Bio-medicine, University of Rome Tor Vergata, 00133 Rome, Italy
8. Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy

Abstract

Muscle synergies simplify motor control by mapping movement goals onto a reduced set of neural commands. However, how this modular architecture adapts during motor learning and the role of explicit strategies remain unclear. Here, we investigated adaptation to “virtual surgeries,” perturbations of muscle pulling directions simulated in virtual reality, with and without explicit cues consisting of touching and naming the muscles most effective. Explicit cues increased reaction times and improved performance after both compatible surgeries, which could be compensated by recombining existing synergies, and incompatible surgeries, which required the exploration of new muscle patterns. After incompatible surgeries, however, performance gains were limited to conditions requiring only minor synergy reorganization. These findings suggest that explicit strategies primarily bias recombination toward existing synergies rather than forming new ones. Understanding the contributions of explicit processes can inform rehabilitation protocols and human-machine interfaces aimed at refining existing movements or acquiring new motor skills.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Code

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Data

Datasets cited

• doi:10.17632/jz4j8nrk4j.1, at the source; found in “Data and code availability”

Data and code availability

• Filtered EMG and force data, and elaborated data have been deposited at the Mendeley Data repository at https://doi.org/10.17632/jz4j8nrk4j.1 and are publicly available as of the date of publication. • Codes used for statistical analyses have been deposited at the Mendeley Data repository at https://doi.org/10.17632/jz4j8nrk4j.1 and are publicly available as of the date of publication. • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Authors: added Daniele Borzelli (0000-0002-9456-0177); removed Daniele Borzelli

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 10 keywords, 80 references.

Cite

This paper

Borzelli, D., De Pasquale, P., Gurgone, S., Mezzetti, M., Quercia, A., Berger, D. J., Dal’Bello, L. R., & d’Avella, A. (2026). An explicit strategy facilitates adaptation to virtual surgeries by biasing the recruitment of existing synergies. iScience, 29(8), 116488. https://doi.org/10.1016/j.isci.2026.116488

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