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.
Reviewing Editor
- Kunlin Wei, Peking University, China
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.
Version history
- Received: December 1, 2020
- Preprint posted: December 2, 2020 (view preprint)
- Accepted: February 11, 2022
- Accepted Manuscript published: February 28, 2022 (version 1)
- Version of Record published: May 4, 2022 (version 2)
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
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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- Neuroscience
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.
