Neural predictors of sensorimotor adaptation rate and savings

Cassady, Kaitlin; Ruitenberg, M.F.L.; Koppelmans, Vincent; Reuter‐Lorenz, Patricia A.; Dios, Yiri De; Gadd, N. E.; Wood, Scott J.; Riascos, Roy; Kofman, I. S.; Bloomberg, Jacob J.; Mulavara, Ajitkumar P.; Seidler, Rachael D.

doi:10.1002/hbm.23924

Cited by 29 publications

(11 citation statements)

References 71 publications

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“…As several participants would occasionally attempt to guess where the target would appear and move the joystick in a (wrong) direction of their choice without attending to the target location, we replaced trials where the DE deviated more than 2.5 standard deviations from the mean across a session by the mean of the directly preceding and succeeding trial to minimize the influence of such trials (cf. 5 , 31 ). This was done separately for each of the four sessions per participant and resulted in the replacement of 1.8% of the trials overall.…”

Section: Methodsmentioning

confidence: 99%

Neural correlates of multi-day learning and savings in sensorimotor adaptation

Ruitenberg

Koppelmans

Dios

et al. 2018

Sci Rep

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In the present study we evaluated changes in neural activation that occur over the time course of multiple days of sensorimotor adaptation, and identified individual neural predictors of adaptation and savings magnitude. We collected functional MRI data while participants performed a manual adaptation task during four separate test sessions over a three-month period. This allowed us to examine changes in activation and associations with adaptation and savings at subsequent sessions. Participants exhibited reliable savings of adaptation across the four sessions. Brain activity associated with early adaptation increased across the sessions in a variety of frontal, parietal, cingulate, and temporal cortical areas, as well as various subcortical areas. We found that savings was positively associated with activation in several striatal, parietal, and cingulate cortical areas including the putamen, precuneus, angular gyrus, dorsal anterior cingulate cortex (dACC), and cingulate motor area. These findings suggest that participants may learn how to better engage cognitive processes across days, potentially reflecting improvements in action selection. We propose that such improvements may rely on action-value assignments, which previously have been linked to the dACC and striatum. As correct movements are assigned a higher value than incorrect movements, the former are more likely to be performed again.

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Section: Methodsmentioning

confidence: 99%

Neural correlates of multi-day learning and savings in sensorimotor adaptation

Ruitenberg

Koppelmans

Dios

et al. 2018

Sci Rep

Self Cite

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“…This means that these studies will misclassify brain activity related to explicit strategy use. It is therefore no small wonder that brain areas commonly implicated in cognitive control and high-level decision making are often observed to be active during in sensorimotor adaptation (Anguera et al 2010; Cassady et al 2017; Ruitenberg et al 2018; Seidler and Noll 2008; Shadmehr and Holcomb 1997).…”

Section: Beyond Behaviormentioning

confidence: 99%

A Synthesis of the Many Errors and Learning Processes of Visuomotor Adaptation

Morehead

Xivry

2021

Preprint

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Visuomotor adaptation has one of the oldest experimental histories in psychology and neuroscience, yet its precise nature has always been a topic of debate. Here we offer a survey and synthesis of recent work on visuomotor adaptation that we hope will prove illuminating for this ongoing dialogue. We discuss three types of error signals that drive learning in adaptation tasks: task performance error, sensory prediction-error, and a binary target hitting error. Each of these errors has been shown to drive distinct learning processes. Namely, both target hitting errors and putative sensory prediction-errors drive an implicit change in visuomotor maps, while task performance error drives learning of explicit strategy use and non-motor decision-making. Each of these learning processes contributes to the overall learning that takes place in visuomotor adaptation tasks, and although the learning processes and error signals are independent, they interact in a complex manner. We outline many task contexts where the operation of these processes is counter-intuitive and offer general guidelines for their control, measurement and interpretation. We believe this new framework unifies several disparate threads of research in sensorimotor adaptation that often seem in conflict. We conclude by explaining how this more nuanced understanding of errors and learning processes could lend itself to the analysis of other types of sensorimotor adaptation, of motor skill learning, of the neural processing underlying sensorimotor adaptation in humans, of animal models and of brain computer interfaces.

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“…The overarching function of dACC is to deploy cognitive control via its interactions with the lateral prefrontal cortex (for review see Kolling, Behrens, Wittmann, & Rushworth, 2016). Greater functional as well as structural connectivity between dACC and other key regions involved in sensorimotor adaptation to visual perturbations were further associated with faster early adaptation, when the task is more cognitively demanding (Cassady et al, 2017). Here, a graded response to errors of different sizes might therefore allow cognitive control mechanisms to be allocated to facilitate force field adaptation.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Greater neural responses to trajectory errors are associated with superior force field adaptation in older adults

Reuter

Pearcey

Carroll

2018

Experimental Gerontology

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Although age-related declines in cognitive, sensory and motor capacities are well documented, current evidence is mixed as to whether or not aging impairs sensorimotor adaptation to a novel dynamic environment. More importantly, the extent to which any deficits in sensorimotor adaptation are due to general impairments in neural plasticity, or impairments in the specific processes that drive adaptation is unclear. Here we investigated whether there are age-related differences in electrophysiological responses to reaching endpoint and trajectory errors caused by a novel force field, and whether markers of error processing relate to the ability of older adults to adapt their movements. Older and young adults (N = 24/group, both sexes) performed 600 reaches to visual targets, and received audio-visual feedback about task success or failure after each trial. A velocity-dependent curl field pushed the hand to one side during each reach. We extracted ERPs time-locked to movement onset [kinematic error-related negativity (kERN)], and the presentation of success/failure feedback [feedback error-related negativity (fERN)]. At a group level, older adults did not differ from young adults in the rate or extent of sensorimotor adaptation, but EEG responses to both trajectory errors and task errors were reduced in the older group. Most interestingly, the amplitude of the kERN correlated with the rate and extent of sensorimotor adaptation in older adults. Thus, older adults with an impaired capacity for encoding kinematic trajectory errors also have compromised abilities to adapt their movements in a novel dynamic environment.

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Neural predictors of sensorimotor adaptation rate and savings

Cited by 29 publications

References 71 publications

Neural correlates of multi-day learning and savings in sensorimotor adaptation

Neural correlates of multi-day learning and savings in sensorimotor adaptation

A Synthesis of the Many Errors and Learning Processes of Visuomotor Adaptation

Greater neural responses to trajectory errors are associated with superior force field adaptation in older adults

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