Implicit sensorimotor adaptation comprises distinct mechanisms for action selection and execution
4 affiliations
- 1Department of Psychology, University of California, Berkeley, California, United States of America
- 2Department of Neuroscience, University of California, Berkeley, California, United States of America
- 3Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- 4Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
Abstract
The sensorimotor system is continuously adjusted to minimize error. Current theories assume that this adaptation process entails the operation of multiple learning systems, with a key division between implicit and explicit components. Recent studies have revealed several inconsistencies regarding the characteristics and constraints of the implicit system, suggesting that the current framework is incomplete. Here, we propose that these conflicting findings can be understood by recognizing that there are multiple implicit subcomponents, each with distinct computational goals. One well-studied component is implicit recalibration, a process critical for action execution which uses sensory-prediction errors to automatically refine the sensorimotor map. In the current study, we describe a second, novel component, implicit aiming, a process which contributes to action selection to achieve specific goals. Through a series of studies using human participants, we find compelling evidence that those two implicit processes show a clear separation in their temporal stabilities and contextual modulations. These distinct properties correspond to different computational frameworks attributing learning dynamics to either contextual inference or cancellation of competing neural populations, respectively. Together, these findings suggest an alternative framework for sensorimotor adaptation based on the computational goals of the system rather than phenomenology.
Sensorimotor adaptation recalibrates the mapping between motor commands and movement to compensate for unexpected errors induced by external or internal factors. This study describes implicit aiming as a distinct process that operates in sensorimotor learning alongside implicit recalibration and explicit aiming, arguing for revising the current framework for classifying motor learning.
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BibTeX
@article{wang2026implicit,
title = {{Implicit sensorimotor adaptation comprises distinct mechanisms for action selection and execution}},
author = {Tianhe Wang and Tony Lam and Jordan A. Taylor and Richard B. Ivry},
journal = {PLOS Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pbio.3004057},
url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3004057},
note = {CC BY},
}
RIS
TY - JOUR
TI - Implicit sensorimotor adaptation comprises distinct mechanisms for action selection and execution
AU - Tianhe Wang
AU - Tony Lam
AU - Jordan A. Taylor
AU - Richard B. Ivry
PY - 2026
DA - 2026/10/01
JO - PLOS Biology
DO - 10.1371/journal.pbio.3004057
UR - https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3004057
AB - The sensorimotor system is continuously adjusted to minimize error. Current theories assume that this adaptation process entails the operation of multiple learning systems, with a key division between implicit and explicit components. Recent studies have revealed several inconsistencies regarding the characteristics and constraints of the implicit system, suggesting that the current framework is incomplete. Here, we propose that these conflicting findings can be understood by recognizing that there are multiple implicit subcomponents, each with distinct computational goals. One well-studied component is implicit recalibration, a process critical for action execution which uses sensory-prediction errors to automatically refine the sensorimotor map. In the current study, we describe a second, novel component, implicit aiming, a process which contributes to action selection to achieve specific goals. Through a series of studies using human participants, we find compelling evidence that those two implicit processes show a clear separation in their temporal stabilities and contextual modulations. These distinct properties correspond to different computational frameworks attributing learning dynamics to either contextual inference or cancellation of competing neural populations, respectively. Together, these findings suggest an alternative framework for sensorimotor adaptation based on the computational goals of the system rather than phenomenology. Sensorimotor adaptation recalibrates the mapping between motor commands and movement to compensate for unexpected errors induced by external or internal factors. This study describes implicit aiming as a distinct process that operates in sensorimotor learning alongside implicit recalibration and explicit aiming, arguing for revising the current framework for classifying motor learning.
SN - 1545-7885
SN - 1544-9173
ER -