Neural predictors of sensorimotor adaptation rate and savings.
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ABSTRACT:
SUBMITTER: Cassady K
PROVIDER: S-EPMC5847457 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Error detection and correction are essential components of motor skill learning. These processes have been well characterized in cognitive psychology using electroencephalography (EEG) to record an event-related potential (ERP) called error-related negativity (ERN). However, it is unclear whether this ERP component is sensitive to the magnitude of the error made in a sensorimotor adaptation task. In the present study, we tested the function of error-related activity in a visuomotor adaptation task. To examine whether error size is reflected in the ERP, two groups of participants adapted manual aiming movements to either a small (30 degrees) or large (45 degrees) rotation of the visual feedback display. Each participant's trials were sorted into large and small error trials using a median split to examine potential error magnitude waveform differences. We also examined these trial types at the early and late stages of adaptation. There were no group differences for the behavioral or neural measures; however, waveforms from large error trials were significantly different from small error trials. The waveforms also changed as a function of practice as early adaptation waveforms were larger than late adaptation waveforms. The observed ERP component reflected differences in error magnitude with the amount of activity corresponding to the size of the error. Movement monitoring potentials likely affected the frequency and time course of the waveform so that it did not resemble the typical ERN; however, error-related activity was still distinguishable. The present findings are discussed in terms of current theories of the ERN as well as skill acquisition.
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Project description:Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across 2 days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this "cognitive network" with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster learning, consistent with the notion that declarative memory systems are involved with fast relearning of sensorimotor mappings. Together, these findings provide novel evidence for the role of higher-order brain systems in driving variability in adaptation.
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