About

Amy Bastian is a Professor of Neuroscience at Johns Hopkins University, affiliated with the Kennedy Krieger Institute. Her laboratory focuses on the mechanisms of human movement disorders, particularly those resulting from neurological damage or disease of the central nervous system. Her research aims to understand how different types of movement disorders develop, how treatments can improve movement, and the processes involved in learning new movements and recovery following brain damage. Her work has extensively studied how damage to the cerebellum causes movement incoordination or ataxia, as well as locomotor disorders in individuals with stroke. She also investigates visuomotor control and learning in children with autism. Her laboratory employs various techniques to quantify movement, including 3-dimensional tracking, muscle activity recordings, force plate recordings, and calculations of joint forces and torques. The research utilizes novel devices such as split belt treadmills, KinArm Robots, and virtual environments to test learning and measure movement performance with high precision. Her interdisciplinary approach involves collaboration with neurologists, physical therapists, biomedical engineers, and neuroscientists to advance understanding of movement control and recovery.

Research topics

• Psychology
• Neuroscience
• Physical medicine and rehabilitation
• Computer Science
• Medicine
• Artificial Intelligence
• Engineering
• Control engineering

Selected publications

• Automatic learning mechanisms for flexible human locomotion

  eLife · 2026-02-03

  articleOpen accessSenior author

  Movement flexibility and automaticity are necessary to successfully navigate different environments. When encountering difficult terrains such as a muddy trail, we can change how we step almost immediately so that we can continue walking. This flexibility comes at a cost since we initially must pay deliberate attention to how we are moving. Gradually, after a few minutes on the trail, stepping becomes automatic so that we do not need to think about our movements. Canonical theory indicates that different adaptive motor learning mechanisms confer these essential properties to movement: explicit control confers rapid flexibility, while forward model recalibration confers automaticity. Here, we uncover a distinct mechanism of treadmill walking adaptation – an automatic stimulus-response mapping – that confers both properties to movement. The mechanism is flexible as it learns stepping patterns that can be rapidly changed to suit a range of treadmill configurations. It is also automatic as it can operate without deliberate control or explicit awareness by the participants. Our findings reveal a tandem architecture of forward model recalibration and automatic stimulus-response mapping mechanisms for walking, reconciling different findings of motor adaptation and perceptual realignment.

  PublisherDOI

• Striatal and cerebellar interactions during reward-based motor performance

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-06 · 2 citations

  preprintOpen access

  Goal-directed motor performance relies on the brain's ability to distinguish between actions that lead to successful and unsuccessful outcomes. The basal ganglia (BG) and cerebellum (CBL) are integral to processing performance outcomes, yet their functional interactions remain underexplored. This study scanned participants' brains with functional magnetic imaging (fMRI) while they performed a skilled motor task for monetary rewards, where outcomes depended on their motor performance and also probabilistic events that were not contingent on their performance. We found successful motor outcomes increased activity in the ventral striatum (VS), a functional sub-region of the BG, whereas unsuccessful motor outcomes engaged the CBL. In contrast, for probabilistic outcomes unrelated to motor performance, the BG and CBL exhibited no differences in activity between successful and unsuccessful outcomes. Dynamic causal modeling revealed that VS-to-CBL connectivity was inhibitory following successful motor outcomes, suggesting that the VS may suppress CBL error processing for correct actions. Conversely, CBL-to-VS connectivity was inhibitory after unsuccessful motor outcomes, potentially preventing reinforcement of erroneous actions. Additionally, interindividual differences in task preference, assessed by having participants choose between performing the motor task or flipping a coin for monetary rewards, were related to inhibitory VS-CBL connectivity. These findings highlight a performance-mediated functional network between the VS and CBL, modulated by motivation and subjective preferences, supporting goal-directed behavior.

  PublisherOA PDFDOI

• Task demands shift motor learning from adaptation to feedback control in a naturalistic bimanual task

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-07

  preprintOpen accessSenior authorCorresponding

  Most purposeful movements require the coordinated control of both hands, yet motor adaptation studies rely on highly constrained tasks that bear little resemblance to everyday bimanual actions. Here, we investigated how task demands shape control strategies during adaptation in a naturalistic bimanual object manipulation task. We tested 73 participants who lifted a virtual plate while we systematically distorted visual feedback of their right hand's movement, creating a sensory conflict between arms. Compared to unimanual, bimanual lifting shifted learning away from feedforward adaptation toward use of feedback control-participants moved more slowly with gradual speed scaling, developed compensatory hand adjustments, and showed smaller aftereffects. Relaxing precision demands improved success and reduced feedback reliance, while minimizing interlimb sensory conflict diminished compensatory adjustments and restored plate aftereffects to unimanual levels. Bimanual contexts create distinct learning environments where precision demands and interlimb sensory conflict independently shape control strategy; this may inform bimanual training protocols.

  PublisherOA PDFDOI

• Reinforcement Learning is Impaired in the Sub-acute Post-stroke Period

  Neurorehabilitation and neural repair · 2025-01-23 · 5 citations

  article

  Background: In humans, most spontaneous recovery from motor impairment after stroke occurs in the first 3 months. Studies in animal models show higher responsiveness to training over a similar time-period. Both phenomena are often attributed to a milieu of heightened plasticity, which may share some mechanistic overlap with plasticity associated with normal motor learning. Objective: Given that neurorehabilitation approaches are frequently predicated on motor learning principles, here we asked if the sensitivity of trial-to-trial learning for 2 kinds of motor learning processes often involved during rehabilitation is also enhanced early post-stroke. In a cross-sectional design, we compared (1) reinforcement and (2) error-based learning in 2 groups: 1 tested within 3 months after stroke (early group, N = 35) another tested more than 6 months after stroke (late group, N = 30). These 2 forms of motor learning were assessed with variations of the same visuomotor rotation task. Critically, motor execution was matched between the 2 groups. Results: Reinforcement learning was impaired in the early but not the late group, whereas error-based learning was unimpaired in either group. These findings could not be attributed to differences in baseline execution, cognitive impairment, gender, age, or lesion volume and location. Discussion: The presence of a deficit in reinforcement motor learning in the first 3 months after stroke has important implications for rehabilitation. Conclusion: It might be necessary to either increase reinforcement feedback given early after stroke, increase the dose of rehabilitation to compensate, or delay onset of rehabilitation approaches that may rely on reinforcement, for example, constraint-induced movement therapy, and instead emphasize other forms of motor training in the subacute time period.

  PublisherDOI

• Author response: Age-dependent predictors of effective reinforcement motor learning across childhood

  2025-08-28

  peer-reviewOpen accessSenior author

  Reinforcement motor learning of probabilistic tasks shows a protracted developmental trajectory in childhood due to high motor noise and low exploration, though performance deficits can be ameliorated in younger children by reducing task demands in spatial processing and probabilistic reasoning.

  PublisherDOI

• The control of movement gradually transitions from feedback control to feedforward adaptation throughout childhood

  npj Science of Learning · 2025-03-11 · 8 citations

  articleOpen accessSenior author

  The ability to adjust movements in response to perturbations is key for an efficient and mature nervous system, which relies on two complementary mechanisms - feedforward adaptation and feedback control. We examined the developmental trajectory of how children employ these two mechanisms using a previously validated visuomotor rotation task, conducted remotely in a large cross-sectional cohort of children aged 3-17 years and adults (n = 656; 353 males & 303 females). Results revealed a protracted developmental trajectory, with children up to ~13-14 years showing immature adaptation. Younger children relied more on feedback control to succeed. When adaptation was the only option, they struggled to succeed, highlighting a limited ability to adapt. Our results show a gradual shift from feedback control to adaptation learning throughout childhood. We also generated percentile curves for adaptation and overall performance, providing a reference for understanding the development of motor adaptation and its trade-off with feedback control.

  PublisherOA PDFDOI

• Feedback and feedforward control are differentially delayed in cerebellar ataxia

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-10 · 4 citations

  preprintOpen access

  Damage to the cerebellum can cause ataxia, a condition associated with impaired movement coordination. Typically, coordinated movement relies on a combination of anticipatory mechanisms (specifically, feedforward control) and corrective mechanisms (embodied by feedback control). Here, we show that in 3D reaching in VR, ataxia preserves the visuomotor feedforward and feedback control structure compared to the control group. However, the ataxia group exhibits a small increase in feedback delay (~ 20 ms) and a substantial increase in feedforward delay (~ 70 ms) together with a reduced feedback gain (~ 25% lower). Our results suggest that the feedforward and feedback pathways remain largely intact in ataxia, but that time delay deficits and temoral discoordination amongst these control pathways may contribute to the disorder. We also find that providing a preview-analogous to driving on a clear night and seeing the road ahead vs. driving in the fog-improves tracking performance in the ataxia group, although the control group was significantly better able to exploit this preview information. Overall, our results indicate that the feedforward control and preview utilization are relatively well-preserved in individuals with cerebellar ataxia, and that preview could potentially be leveraged to enhance the feedforward performance of those with ataxia.

  PublisherDOI

• Age-dependent predictors of effective reinforcement motor learning across childhood

  eLife · 2025-06-10

  preprintOpen accessSenior author

  Abstract Across development, children must learn motor skills such as eating with a spoon and drawing with a crayon. Reinforcement learning, driven by success and failure, is fundamental to such sensori-motor learning. It typically requires a child to explore movement options along a continuum (grip location on a crayon) and learn from probabilistic rewards (whether the crayon draws or breaks). Here, we studied the development of reinforcement motor learning using online motor tasks to engage children aged 3 to 17 years and adults (cross-sectional sample, N=385). Participants moved a cartoon penguin across a scene and were rewarded (animated cartoon clip) based on their final movement position. Learning followed a clear developmental trajectory when participants could choose to move anywhere along a continuum and the reward probability depended on the final movement position. Learning was incomplete or absent in 3 to 8-year-olds and gradually improved to adult-like levels by adolescence. A reinforcement learning model fit to each participant identified two age-dependent factors underlying improvement across development: an increasing amount of exploration after a failed movement and a decreasing level of motor noise. We predicted, and confirmed, that switching to discrete targets and deterministic reward would improve 3 to 8-year-olds’ learning to adult-like levels by increasing exploration after failed movements. Overall, we show a robust developmental trajectory of reinforcement motor learning abilities under ecologically relevant conditions i.e., continuous movement options mapped to probabilistic reward. This learning may be limited by immature spatial processing and probabilistic reasoning abilities in young children and can be rescued by reducing task demands.

  PublisherDOI

• Age-dependent predictors of effective reinforcement motor learning across childhood

  eLife · 2025-07-31

  articleOpen accessSenior author

  Across development, children must learn motor skills such as drawing with a crayon. Reinforcement learning, driven by success and failure, is fundamental to such sensorimotor learning. It typically requires a child to explore movement options along a continuum (grip location on a crayon) and learn from probabilistic rewards (whether the crayon draws or breaks). We studied the development of reinforcement motor learning using online motor tasks to engage children aged 3–17 years and adults (cross-sectional sample, N=385). Participants moved a cartoon penguin across a scene and were rewarded (animated cartoon clip) based on their final movement position. Learning followed a clear developmental trajectory when participants could choose to move anywhere along a continuum and the reward probability depended on the final movement position. Learning was incomplete or absent in 3–8 year-olds and gradually improved to adult-like levels by adolescence. A reinforcement learning model fit to each participant identified two age-dependent factors underlying improvement across development: an increasing amount of exploration after a failed movement and a decreasing level of motor noise. We predicted, and confirmed, that switching to discrete targets and deterministic reward would improve 3–8 year-olds’ learning to adult-like levels by increasing exploration after failed movements. Overall, we show a robust developmental trajectory of reinforcement motor learning abilities under ecologically relevant conditions, that is, continuous movement options mapped to probabilistic reward. This learning may be limited by immature spatial processing and probabilistic reasoning abilities in young children and can be rescued by reducing task demands.

  PublisherDOI

• Author response: Age-dependent predictors of effective reinforcement motor learning across childhood

  2025-06-10

  peer-reviewOpen accessSenior author

  Across development, children must learn motor skills such as eating with a spoon and drawing with a crayon. Reinforcement learning, driven by success and failure, is fundamental to such sensori-motor learning. It typically requires a child to explore movement options along a continuum (grip location on a crayon) and learn from probabilistic rewards (whether the crayon draws or breaks). Here, we studied the development of reinforcement motor learning using online motor tasks to engage children aged 3 to 17 years and adults (cross-sectional sample, N=385). Participants moved a cartoon penguin across a scene and were rewarded (animated cartoon clip) based on their final movement position. Learning followed a clear developmental trajectory when participants could choose to move anywhere along a continuum and the reward probability depended on the final movement position. Learning was incomplete or absent in 3 to 8-year-olds and gradually improved to adult-like levels by adolescence. A reinforcement learning model fit to each participant identified two age-dependent factors underlying improvement across development: an increasing amount of exploration after a failed movement and a decreasing level of motor noise. We predicted, and confirmed, that switching to discrete targets and deterministic reward would improve 3 to 8-year-olds’ learning to adult-like levels by increasing exploration after failed movements. Overall, we show a robust developmental trajectory of reinforcement motor learning abilities under ecologically relevant conditions i.e., continuous movement options mapped to probabilistic reward. This learning may be limited by immature spatial processing and probabilistic reasoning abilities in young children and can be rescued by reducing task demands.

  PublisherDOI

Recent grants

• NIH Grant R01HD048741

  NIH · $2.6M · 2015

• Research Training in Rehabilitation for Brain Injury and Neurological Disability

  NIH · $7.2M · 1991–2029

• Mechanisms and Rehabilitation of Cerebellar Ataxia

  NIH · $453k · 2002–2023

• Human Locomotor Plasticity in Health and Disease

  NIH · $3.3M · 2015–2022

• NIH Grant R13NS103356

  NIH · $20k · 2018

Frequent coauthors

• Ryan T. Roemmich

  Kennedy Krieger Institute

  127 shared

• Pablo Celnik

  Johns Hopkins University

  86 shared

• Luc Mallet

  Institut du Cerveau

  50 shared

• Jérôme Yelnik

  Inserm

  50 shared

• William Haynes

  Centre Hospitalier Universitaire de Montpellier

  49 shared

• Aude Teillant

  Princeton University

  49 shared

• M.-L. Welter

  Centre Hospitalier Universitaire de Rouen

  49 shared

• Carine Karachi

  Assistance Publique – Hôpitaux de Paris

  49 shared

Labs

• Amy Bastian LabPI