Competition between parallel sensorimotor learning systems
- Johns Hopkins School of Medicine, United States
- York University, Canada
- University of Buenos Aires, Argentina
- Johns Hopkins University School of Medicine, United States
Abstract
Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge, and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.
Data availability
Source data files generated or analyzed during this study, as well as the associated analysis code, are included as supplements to Figures 1-10, as well as their associated Figure Supplements, and have also been deposited in OSF under accession code MZS6A
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Competition between parallel sensorimotor learning systemsOSF, Doi 10.17605/OSF.IO/MZS6A.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke (F32NS095706)
- Scott T Albert
National Science Foundation (CNS-1714623)
- Reza Shadmehr
National Institute of Neurological Disorders and Stroke (R01NS078311)
- Reza Shadmehr
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Informed consent was obtained from all study participants. All human subjects work was approved by the Johns Hopkins School of Medicine Institutional Review Board (protocol number NA_00037510) or the York Human Participants Review Sub-committee.
Copyright
© 2022, Albert et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Competition between parallel sensorimotor learning systems
eLife 11:e65361.
https://doi.org/10.7554/eLife.65361
Further reading
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- Neuroscience
When navigating environments with changing rules, human brain circuits flexibly adapt how and where we retain information to help us achieve our immediate goals.
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- Neuroscience
When holding visual information temporarily in working memory (WM), the neural representation of the memorandum is distributed across various cortical regions, including visual and frontal cortices. However, the role of stimulus representation in visual and frontal cortices during WM has been controversial. Here, we tested the hypothesis that stimulus representation persists in the frontal cortex to facilitate flexible control demands in WM. During functional MRI, participants flexibly switched between simple WM maintenance of visual stimulus or more complex rule-based categorization of maintained stimulus on a trial-by-trial basis. Our results demonstrated enhanced stimulus representation in the frontal cortex that tracked demands for active WM control and enhanced stimulus representation in the visual cortex that tracked demands for precise WM maintenance. This differential frontal stimulus representation traded off with the newly-generated category representation with varying control demands. Simulation using multi-module recurrent neural networks replicated human neural patterns when stimulus information was preserved for network readout. Altogether, these findings help reconcile the long-standing debate in WM research, and provide empirical and computational evidence that flexible stimulus representation in the frontal cortex during WM serves as a potential neural coding scheme to accommodate the ever-changing environment.
